Technical Competency Assessment

1. Executive summary

Profile type: Senior / Lead-capable Technical Generalist with deep specialization in CG/VFX, rendering and production pipelines, and substantial practical competence in software engineering, product engineering, automation, marketplace/ERP systems and applied generative AI.

Primary specialization: CG/VFX, rendering, lighting, look development, procedural workflows and technical production pipelines.

Secondary specialization: product-oriented software engineering, backend systems, API integration, workflow automation and domain-specific ERP systems.

Additional strong area: applied LLM and generative-media engineering, particularly integration of existing models into production workflows rather than model research or training.

This is not a conventional narrow software-engineering profile. The strongest differentiator is the ability to move between visual production, software architecture, business processes, external APIs, data models and operational constraints without treating them as separate problems.

The candidate appears strongest in roles where an ambiguous real-world process must be converted into a working technical system.

Typical examples of suitable problem classes:

• production tooling and pipeline engineering;
• technical art and CG infrastructure;
• workflow automation;
• internal products and vertical SaaS;
• systems integrating unreliable or incomplete third-party APIs;
• AI-assisted production tooling;
• technical prototyping where product and engineering decisions are tightly coupled;
• projects requiring one senior engineer to span several traditionally separate disciplines.

The profile should not be interpreted as expert-level competence in every technology mentioned below. The scoring deliberately distinguishes long-term professional depth from subjects that the candidate merely understands or has explored.

2. Rating methodology

Competence is rated on a 0–10 scale.

0–2 — Familiarity
Understands basic terminology or has limited practical exposure.

3–4 — Working awareness
Can perform bounded tasks with documentation or assistance.

5–6 — Independent practitioner
Can independently solve ordinary practical problems and build usable systems in the domain.

7 — Senior practical competence
Can make architecture or implementation decisions independently, debug non-obvious problems and operate with incomplete specifications.

8 — Strong senior / domain specialist
Substantial production depth. Able to design systems, identify hidden constraints, review others' work and solve unusual problems.

9 — Expert specialization
Deep professional experience accumulated over many years. Strong understanding of both production practice and underlying mechanisms.

10 — Field-defining expertise
Research-level or industry-leading expertise. This score is intentionally not assigned here.

Scores are estimates of demonstrated competence, not certifications.

A distinction is made between:

• professional depth — repeated real-world use and responsibility;
• technical literacy — understanding a technology well enough to discuss or use it;
• research interest — asking sophisticated questions without sufficient evidence of professional competence.

3. Competency matrix

4. Detailed assessment

4.1 CG / VFX Production

Score: 9.0 / 10
Confidence: High
Level: Expert specialization

Assessment

This is the candidate's deepest and most mature professional domain.

The level of discussion indicates long-term production experience rather than tool familiarity. Problems are typically formulated in terms of rendering behaviour, sampling, visual quality, pipeline constraints, asset behaviour, renderer differences, production cost and failure modes.

There is a clear distinction between knowing how to operate DCC software and understanding why a production image or pipeline behaves incorrectly.

The candidate is comfortable moving across artistic and engineering concerns.

Evidence pattern

Demonstrated competence includes:

• production rendering;
• lighting;
• look development;
• procedural workflows;
• optimization;
• render troubleshooting;
• asset and scene behaviour;
• render-farm considerations;
• colour-management workflows;
• multi-DCC production pipelines.

The candidate often starts analysis beyond the basic operational layer, which is typical of someone who has already internalized ordinary production workflows.

Limitation

The score does not imply specialist-level mastery of every subdiscipline of CG, such as character animation, rigging or advanced simulation research.

4.2 Rendering / Lighting / Look Development

Score: 9.0 / 10
Confidence: High
Level: Expert specialization

Assessment

Rendering is probably the clearest single specialist competence.

The candidate appears capable of reasoning simultaneously about:

• sampling;
• noise;
• convergence;
• shading;
• lighting;
• colour;
• renderer configuration;
• scene complexity;
• production quality requirements;
• computational cost.

Questions and technical discussions tend to concern trade-offs and renderer behaviour rather than basic setup.

Hiring interpretation

Suitable for senior rendering, lighting TD, lookdev TD, pipeline-facing rendering roles and technically difficult CG production tasks.

4.3 Houdini / Procedural 3D

Score: 8.5 / 10
Confidence: High
Level: Strong senior / specialist

Assessment

Houdini is used as an engineering environment, not merely as an artistic application.

The candidate demonstrates comfort with procedural thinking, data flow, renderer integration and production debugging.

The score is slightly below 9 because there is insufficient evidence to claim equally deep specialization across the entire Houdini ecosystem, particularly areas such as HDK development, solver implementation or highly specialized simulation research.

Hiring interpretation

Clearly senior-level for general Houdini production and technical work.

4.4 CG Pipeline / Technical Art

Score: 8.5 / 10
Confidence: High
Level: Strong senior / specialist

Assessment

Pipeline engineering appears to be a natural bridge between the candidate's visual and software competencies.

The candidate understands production systems as combinations of:

• assets;
• data;
• rendering;
• tools;
• automation;
• execution environments;
• version constraints;
• artist workflows.

This is a particularly valuable competency because technical decisions are evaluated against production impact rather than engineering elegance alone.

4.5 Python / Production Tooling

Score: 8.0 / 10
Confidence: High
Level: Strong senior practical competence

Assessment

Python is clearly a working engineering language rather than an occasional scripting tool.

The candidate appears capable of independently designing and maintaining automation, pipeline utilities, API clients, processing scripts and application logic.

The strongest competence is pragmatic engineering rather than language-theory specialization.

Strengths

• automation;
• API interaction;
• pipeline tooling;
• data processing;
• integration with production applications;
• debugging;
• rapid prototyping.

Limitations

There is insufficient evidence to claim specialist knowledge of Python runtime internals, interpreter implementation or advanced language/compiler work.

4.6 JavaScript

Score: 7.0 / 10
Confidence: High
Level: Senior practical competence

Assessment

The candidate can build real application functionality and reason about architecture rather than merely frontend scripting.

JavaScript appears to be used primarily as a product-development tool.

Limitations

No strong evidence of specialization in:

• JavaScript engine internals;
• advanced type-system design;
• large-scale frontend platform architecture;
• compiler/toolchain engineering.

4.7 Backend Engineering

Score: 7.0 / 10
Confidence: High
Level: Senior practical competence

Assessment

The candidate can independently build and operate backend systems supporting real users and business processes.

Relevant demonstrated concerns include:

• persistent state;
• APIs;
• external integrations;
• synchronization;
• sockets;
• authentication;
• deployment;
• business logic;
• data reconciliation.

The candidate generally reasons about backend systems through domain behaviour rather than framework conventions.

Limitation

There is limited evidence of operating internet-scale backend infrastructure or large engineering organizations.

The score therefore represents strong product backend engineering, rather than high-scale infrastructure specialization.

4.8 Application and Software Architecture

Score: 7.5 / 10
Confidence: High
Level: Strong senior

Assessment

Architecture is one of the more interesting competencies in the profile because it appears to have developed from solving increasingly complex real systems.

The candidate routinely reasons about:

• source of truth;
• ownership of state;
• synchronization;
• state transitions;
• boundaries between services;
• external-system inconsistencies;
• operational complexity;
• deployment constraints;
• failure modes.

There is also a useful tendency to question whether infrastructure complexity is actually justified.

Limitation

Most demonstrated systems appear to be small or medium in organizational scale.

There is insufficient evidence for architecture involving hundreds of engineers, very large distributed systems or multi-year platform migrations.

4.9 Databases and Data Modelling

Score: 6.5 / 10
Confidence: Medium-High
Level: Strong independent practitioner

Assessment

The candidate is comfortable designing application data structures and using relational databases as part of production systems.

Competence appears strongest around application modelling and operational data.

Likely strengths

• schema design;
• state modelling;
• relational application data;
• querying;
• synchronization logic;
• persistence design.

Limitations

No strong evidence of specialist-level knowledge of:

• database engine internals;
• query-planner behaviour;
• advanced replication;
• storage engines;
• large-scale DBA operations.

4.10 API Integration

Score: 8.0 / 10
Confidence: High
Level: Strong senior

Assessment

This deserves to be treated as a separate competency.

The candidate has significant experience integrating external systems whose APIs do not perfectly represent the underlying business process.

The work involves substantially more than HTTP requests.

Typical reasoning includes:

• rate limits;
• incomplete datasets;
• lifecycle mismatches;
• delayed state;
• reconciliation;
• idempotency-like concerns;
• external changes;
• mapping API concepts to actual domain entities.

Hiring interpretation

Particularly suitable for products whose complexity comes from integrating several imperfect external systems.

4.11 ERP / Marketplace Domain Engineering

Score: 8.0 / 10
Confidence: High
Level: Domain specialist

Assessment

The candidate has developed substantial domain understanding around inventory and marketplace operations.

Relevant conceptual areas include:

• physical inventory;
• virtual inventory;
• multiple warehouse representations;
• order fulfilment;
• FBS/FBW-type logistics;
• supply planning;
• stock allocation;
• pricing;
• product visibility;
• labelling;
• assembly;
• purchasing recommendations;
• multi-channel synchronization.

A strong indicator of maturity is the ability to distinguish the real physical process from the simplified model exposed by an external platform API.

Hiring interpretation

Potentially valuable in logistics, commerce, ERP, marketplace tooling and operational SaaS.

4.12 Product Engineering

Score: 7.5 / 10
Confidence: High
Level: Strong senior

Assessment

The candidate tends to approach software through the complete chain:

user process → domain model → interface → automation → implementation → operational cost.

This is more characteristic of a product engineer than of a narrowly scoped software developer.

There is evidence of independently taking poorly specified operational problems and converting them into functioning systems.

Limitation

The candidate has a strong engineering bias.

Technically interesting problems can occasionally receive more investment than the underlying market hypothesis warrants.

This makes early validation and scope discipline particularly important.

4.13 Product Discovery

Score: 6.5 / 10
Confidence: Medium-High
Level: Strong applied competence

Assessment

The candidate is capable of analyzing user workflows, identifying operational pain and translating it into product functionality.

There is also awareness of pricing, adoption and cost of ownership.

Limitation

Product discovery is less mature than product engineering.

The candidate's default instinct remains to understand and solve the system deeply, which can result in premature engineering before demand is fully validated.

4.14 Applied LLM Engineering

Score: 7.5 / 10
Confidence: High
Level: Strong practitioner

Assessment

The candidate has strong practical understanding of using language models as software components.

Relevant competence includes:

• local inference;
• model selection;
• quantization;
• context limitations;
• tool use;
• agents;
• RAG concepts;
• memory architecture;
• model routing;
• GPU constraints;
• integration of LLMs with applications and workflows.

The candidate understands many operational limitations that are often missed by developers whose LLM knowledge is limited to API calls.

Important distinction

This score applies to LLM application engineering, not machine-learning research.

4.15 Machine Learning Theory

Score: 5.0 / 10
Confidence: Medium
Level: Working technical knowledge

Assessment

The candidate understands modern ML systems well enough to use them intelligently and discuss architecture and inference behaviour.

There is not enough evidence to classify the candidate as an ML researcher or specialist ML engineer.

Areas not demonstrated at specialist depth

• optimization theory;
• training large neural networks;
• mathematical treatment of transformers;
• statistical learning theory;
• distributed training;
• model architecture research;
• rigorous model evaluation research.

Hiring interpretation

Suitable for applied AI product work.

Should not be represented as a research ML specialist.

4.16 Applied Generative Image / Video Engineering

Score: 7.5 / 10
Confidence: High
Level: Strong practitioner

Assessment

The candidate has meaningful production competence with generative visual systems.

This extends beyond prompting.

Relevant understanding includes:

• conditioning;
• model constraints;
• image-to-image workflows;
• LoRA;
• motion transfer;
• workflow graphs;
• VRAM constraints;
• temporal artefacts;
• consistency problems;
• interaction between generated material and conventional CG/VFX pipelines.

Hiring interpretation

Useful for teams building AI-assisted media production or hybrid CG/generative workflows.

4.17 Computer Vision / 3D Reconstruction

Score: 6.5 / 10
Confidence: Medium
Level: Strong applied competence

Assessment

The candidate understands modern reconstruction workflows well enough to use and troubleshoot them as part of production.

Relevant areas include:

• camera reconstruction;
• photogrammetry concepts;
• feature-based reconstruction workflows;
• neural and splat-based scene representations;
• reconstruction quality metrics;
• capture strategy.

Limitation

There is insufficient evidence of research-level depth in:

• projective geometry;
• bundle adjustment mathematics;
• calibration research;
• feature-descriptor research;
• optimization algorithms.

4.18 Gaussian Splatting

Score: 6.5 / 10
Confidence: Medium
Level: Strong applied competence

Assessment

The candidate understands Gaussian Splatting as part of a larger reconstruction and rendering pipeline rather than as a black-box application.

Practical concerns include:

• capture quality;
• source frames;
• reconstruction stages;
• training;
• PSNR and related quality indicators;
• viewers;
• editing and production integration.

This is applied competence rather than research specialization.

4.19 DevOps / Infrastructure

Score: 6.5 / 10
Confidence: High
Level: Strong independent practitioner

Assessment

The candidate can deploy and operate the infrastructure required for his own applications.

Relevant experience includes:

• Linux and Windows environments;
• cloud/VPS deployment;
• reverse-proxy/CDN infrastructure;
• networking;
• remote access;
• process deployment;
• application monitoring concerns;
• storage and filesystem issues.

Limitation

The candidate should not be presented as a dedicated SRE or cloud-platform specialist.

There is limited evidence of deep expertise with very large Kubernetes environments, multi-region systems or high-scale infrastructure operations.

4.20 Networking / Distributed Systems

Score: 6.0 / 10
Confidence: Medium
Level: Independent practitioner

Assessment

The candidate has good working understanding of practical networking and distributed application behaviour.

Networking problems are usually approached through protocol behaviour, topology and measurements rather than guesswork.

Limitation

There is less evidence of deep formal expertise in:

• consensus systems;
• advanced distributed consistency models;
• routing protocols;
• kernel networking;
• large-scale distributed-system design.

This is an important potential growth area because current application work increasingly intersects with these topics.

4.21 Frontend Engineering

Score: 6.0 / 10
Confidence: Medium
Level: Independent practitioner

Assessment

The candidate is capable of implementing functional product interfaces and reasoning about frontend behaviour.

Frontend appears to be a means of delivering products rather than a principal specialization.

Limitation

No strong evidence of deep specialization in large frontend architectures, design systems or browser/runtime internals.

4.22 UX / Product Interface Design

Score: 6.0–6.5 / 10
Confidence: Medium
Level: Strong applied competence

Assessment

The candidate has good instinct for operational interfaces and frequently evaluates whether interface states clearly communicate what the system is doing.

This is particularly useful in workflow-heavy applications.

The skill appears to originate partly from production-tool experience, where unclear state and poor feedback immediately affect users.

Limitation

There is insufficient evidence to classify the candidate as a specialist UX researcher or product designer.

4.23 Hardware / Operating Systems / Technical Troubleshooting

Score: 7.0 / 10
Confidence: High
Level: Senior practical competence

Assessment

Strong practical systems literacy.

The candidate is comfortable diagnosing:

• PC hardware;
• GPU memory and thermal behaviour;
• storage;
• BIOS;
• Windows/Linux interaction;
• drivers;
• codecs;
• networking;
• filesystem and permission problems.

A notable characteristic is that troubleshooting questions often arrive after several variables have already been isolated experimentally.

4.24 Game Development

Score: 5.5 / 10
Confidence: Medium
Level: Independent practitioner

Assessment

The candidate understands game-development concepts and has practical experience with engines and game prototypes.

Relevant areas include:

• gameplay systems;
• multiplayer concepts;
• UI;
• engine workflows;
• networking concerns.

Limitation

There is insufficient evidence of a sustained professional game-development track record comparable to the candidate's VFX or software work.

4.25 Real-Time Graphics

Score: 5.5 / 10
Confidence: Medium
Level: Independent practitioner

Assessment

Strong general CG knowledge provides a useful foundation for real-time work.

However, offline rendering expertise should not automatically be treated as expert knowledge of modern real-time rendering.

Specialist depth has not been demonstrated in areas such as:

• GPU architecture;
• frame graphs;
• temporal reconstruction;
• engine renderer architecture;
• graphics API internals.

4.26 Audio Production

Score: 7.0 / 10
Confidence: Medium
Level: Senior practical competence

Assessment

The candidate has long-term practical experience with DAWs and music-production tools.

This appears to be genuine production literacy rather than casual familiarity.

Limitation

There is insufficient evidence to infer equivalent expertise in acoustic engineering, DSP, professional mastering or studio systems design.

4.27 Data Analysis / Statistics

Score: 5.5 / 10
Confidence: Medium
Level: Independent practitioner

Assessment

The candidate is numerically literate and capable of working with operational metrics, business data and technical measurements.

Strengths

• interpreting metrics;
• constructing operational calculations;
• comparing scenarios;
• working with empirical measurements.

Limitation

Formal statistical methodology appears less developed than engineering analysis.

Potential gaps include:

• causal inference;
• experimental design;
• uncertainty estimation;
• sampling methodology;
• statistical testing.

4.28 Business / Unit Economics

Score: 5.5 / 10
Confidence: Medium
Level: Independent practitioner

Assessment

The candidate can reason about:

• SaaS pricing;
• gross economics;
• operational cost;
• cost of ownership;
• salary or contractor economics;
• product viability.

This is useful business literacy for a technical product engineer.

Limitation

There is no basis for assuming professional competence in accounting, corporate finance or financial modelling.

4.29 Information Security

Score: 4.5 / 10
Confidence: Medium
Level: Working awareness

Assessment

The candidate understands ordinary application-security concerns and operational risks.

There is not enough evidence to claim professional security specialization.

A security-sensitive system should therefore still receive dedicated security review.

4.30 Electronics / Embedded Systems

Score: 4.5 / 10
Confidence: Low-Medium
Level: Working awareness

Assessment

General technical literacy is evident, but there is insufficient evidence of deep professional competence in circuit design, PCB engineering, embedded firmware or signal integrity.

This should not be inferred from general hardware competence.

5. Cross-domain capabilities

Several capabilities are stronger than the individual technology scores suggest.

5.1 Systems decomposition

Score: 8.0 / 10

The candidate is effective at converting poorly specified real-world processes into:

• entities;
• states;
• transitions;
• constraints;
• ownership boundaries;
• automated operations.

This capability appears consistently across unrelated technical domains.

5.2 Debugging

Score: 8.0 / 10

Debugging is a significant strength.

Typical behaviour includes:

1. isolating variables;
2. collecting observable behaviour;
3. identifying boundaries between components;
4. constructing hypotheses;
5. testing hypotheses;
6. narrowing the problem before requesting external input.

This substantially reduces the support burden normally associated with cross-disciplinary engineers.

5.3 Learning velocity

Estimated score: 8.0 / 10

The candidate can rapidly construct useful mental models of unfamiliar technologies and become operationally productive.

This is particularly valuable in emerging or poorly documented domains.

Risk

Fast acquisition of practical understanding can occasionally create confidence ahead of deep theoretical understanding.

The candidate generally compensates for this by actively probing inconsistencies and asking mechanism-level questions.

5.4 Cross-domain transfer

Estimated score: 8.0 / 10

One of the strongest characteristics of the profile is transferring useful abstractions between disciplines.

Examples of transferable thinking include:

• procedural CG → data-flow systems;
• production pipelines → software pipelines;
• asset state → application state;
• physical inventory → domain state machines;
• rendering/debugging methodology → software diagnosis;
• generative media → hybrid deterministic/probabilistic pipelines.

This cross-domain transfer is likely a major source of productivity.

6. Engineering style

The candidate's engineering style can be summarized as:

mechanism-oriented, pragmatic, exploratory, product-aware and strongly biased toward owning the complete system.

He generally prefers understanding how a system behaves internally rather than relying exclusively on abstractions supplied by vendors or frameworks.

He is comfortable working below the level normally required to complete a feature when doing so resolves uncertainty or increases control.

This is particularly useful for:

• unusual technical failures;
• immature tooling;
• undocumented APIs;
• prototype development;
• production automation;
• systems crossing several technical domains.

There is a corresponding risk of overengineering technically interesting problems.

The candidate can build infrastructure faster than many teams can specify it, which occasionally makes implementation cheaper psychologically than validating whether the implementation is necessary.

A strong technical/product counterpart should therefore challenge scope rather than implementation ability.

7. Seniority assessment

A single seniority label is somewhat misleading because the profile is asymmetric.

Expert / very strong senior

• CG/VFX production
• rendering
• lighting/lookdev
• technical CG pipelines

Strong senior

• technical problem solving
• Python production tooling
• API integrations
• product engineering
• application architecture
• vertical ERP/domain engineering
• applied AI integration

Senior-capable / independent

• backend engineering
• JavaScript
• databases
• infrastructure
• frontend
• practical networking

Applied practitioner

• computer vision
• Gaussian Splatting
• game development
• real-time graphics
• statistics
• business analysis

Technical familiarity rather than specialist competence

• ML research/theory
• information security
• electronics/embedded systems

8. Suitable roles

The profile is unusually well suited to roles such as:

Senior Technical Artist / Pipeline TD
Very strong fit.

Senior Rendering / Lighting TD
Very strong fit.

CG Pipeline Engineer
Very strong fit.

Senior Product Engineer for technically complex vertical software
Strong fit.

Technical Product Engineer / Founding Engineer
Strong fit where breadth and autonomy matter.

R&D Engineer for media / VFX / generative production
Strong fit.

Applied AI Engineer
Strong fit when the role is focused on building systems using existing models.

Workflow Automation / Integration Engineer
Strong fit.

Technical Lead for a small cross-functional engineering effort
Potentially strong fit, particularly where technical direction and implementation remain closely connected.

9. Roles that would be a poor interpretation of the profile

The candidate should not automatically be marketed as:

• research ML engineer;
• pure data scientist;
• dedicated cybersecurity engineer;
• embedded engineer;
• specialized database engineer;
• specialized SRE;
• kernel/networking engineer;
• narrow frontend specialist;
• pure people-manager engineering lead.

These areas may be adjacent to the candidate's work, but available evidence does not support equivalent specialist depth.

10. Organizational fit

The candidate is likely to provide the highest leverage in a small or medium senior team where problem boundaries are incomplete.

Good environments include:

• technically ambitious startups;
• R&D groups;
• production technology teams;
• media technology;
• CG/VFX infrastructure;
• internal tooling;
• vertical SaaS;
• commerce/logistics automation;
• applied generative AI.

The candidate is less differentiated in environments where work is intentionally decomposed into very narrow tickets with rigid ownership boundaries.

A major part of the value comes from seeing the connections between components that would otherwise belong to different specialists.

11. Main professional differentiator

The key differentiator is not simply breadth.

The profile has a clear T-shaped / π-shaped structure:

deep vertical:
CG → lighting → rendering → procedural systems → VFX pipeline.

second engineering vertical:
software → APIs → backend → domain modelling → product systems.

horizontal layer:
AI, infrastructure, UX, reconstruction, hardware and operational understanding.

This means the candidate can often follow a problem unusually far through the stack.

For example, the same person can reason about:

• what the user is trying to accomplish;
• how the workflow should behave;
• how the domain should be represented;
• what an external API actually exposes;
• how backend state should work;
• how the operator should interact with it;
• how it should be deployed;
• why a visual or computational result is failing.

That combination is rarer and more relevant than maximizing any one secondary skill score.

12. Primary growth areas

If the goal is to strengthen the software-engineering side of the profile, the highest-leverage areas are fundamental rather than framework-specific:

1. distributed-systems theory;
2. concurrency;
3. transactional semantics and consistency models;
4. database internals;
5. networking fundamentals at greater depth;
6. systematic automated testing;
7. observability;
8. application-security boundaries.

The candidate already encounters problems from these disciplines in practical work.

Greater formal depth would turn some current intuition into repeatable engineering methodology.

Further specialization in arbitrary frameworks is likely to provide lower marginal value.

13. Overall assessment

Overall classification:

Senior multidisciplinary technical engineer with expert-level CG/VFX specialization and strong product/software engineering capability.

The candidate is not best represented as a traditional full-stack engineer who happens to know CG, nor as a VFX artist who learned scripting.

The more accurate interpretation is a technical specialist who developed from high-end visual production into broader systems and product engineering.

The strongest hiring case is therefore based on problem ownership and cross-domain leverage rather than matching a narrow technology checklist.

For conventional software-engineering positions, the candidate should be evaluated as a senior practical engineer with unusually broad adjacent competence, while accounting for less formal depth in some traditional computer-science areas.

For CG/VFX, rendering, pipeline and media-technology roles, the profile is significantly stronger and closer to expert level.

For hybrid roles connecting CG, software, automation, AI and production systems, the combined profile is likely more valuable than the individual ratings imply.

14. Machine-readable summary

candidate_profile:
  archetype: "Senior multidisciplinary technical generalist"
  primary_identity:
    - "CG/VFX technical specialist"
    - "Rendering and lighting specialist"
    - "Pipeline / technical-art engineer"
  secondary_identity:
    - "Product engineer"
    - "Backend / integration engineer"
    - "Applied AI engineer"

core_strengths:
  - domain: "CG/VFX Production"
    score: 9.0
    confidence: high
  - domain: "Rendering / Lighting / Lookdev"
    score: 9.0
    confidence: high
  - domain: "Houdini / Procedural 3D"
    score: 8.5
    confidence: high
  - domain: "CG Pipeline / Technical Art"
    score: 8.5
    confidence: high
  - domain: "Python / Production Tooling"
    score: 8.0
    confidence: high
  - domain: "API Integration"
    score: 8.0
    confidence: high
  - domain: "ERP / Marketplace Domain Engineering"
    score: 8.0
    confidence: high
  - domain: "Debugging"
    score: 8.0
    confidence: high
  - domain: "Systems Decomposition"
    score: 8.0
    confidence: high

strong_secondary_skills:
  - domain: "Application Architecture"
    score: 7.5
  - domain: "Product Engineering"
    score: 7.5
  - domain: "Applied LLM Engineering"
    score: 7.5
  - domain: "Generative Media Engineering"
    score: 7.5
  - domain: "Backend Engineering"
    score: 7.0
  - domain: "JavaScript"
    score: 7.0
  - domain: "Hardware / OS Troubleshooting"
    score: 7.0

working_competence:
  - domain: "Databases"
    score: 6.5
  - domain: "DevOps / Infrastructure"
    score: 6.5
  - domain: "Computer Vision / Reconstruction"
    score: 6.5
  - domain: "Gaussian Splatting"
    score: 6.5
  - domain: "Product Discovery"
    score: 6.5
  - domain: "Networking / Distributed Systems"
    score: 6.0
  - domain: "Frontend Engineering"
    score: 6.0
  - domain: "Game Development"
    score: 5.5
  - domain: "Real-Time Graphics"
    score: 5.5
  - domain: "Data Analysis / Statistics"
    score: 5.5
  - domain: "Business / Unit Economics"
    score: 5.5

non_specialist_domains:
  - domain: "Machine Learning Theory"
    score: 5.0
  - domain: "Electronics / Embedded"
    score: 4.5
  - domain: "Information Security"
    score: 4.5

preferred_problem_types:
  - "ambiguous cross-domain technical problems"
  - "production tooling"
  - "workflow automation"
  - "API-heavy systems"
  - "vertical SaaS"
  - "media technology"
  - "CG/VFX infrastructure"
  - "applied generative AI"
  - "technical prototypes"
  - "systems requiring end-to-end ownership"

strongest_meta_skills:
  systems_decomposition: 8.0
  debugging: 8.0
  learning_velocity: 8.0
  cross_domain_transfer: 8.0

risk_factors:
  - "can overengineer technically interesting problems"
  - "breadth should not be mistaken for specialist depth in every adjacent domain"
  - "software engineering knowledge is partly practice-driven rather than classically CS-driven"
  - "works best when allowed meaningful technical ownership"

best_fit:
  - "Senior / Lead CG Pipeline Engineer"
  - "Senior Technical Artist / TD"
  - "Rendering / Lighting TD"
  - "Senior Product Engineer"
  - "Technical Product Engineer"
  - "Founding Engineer in technically complex product"
  - "Applied AI / Media R&D Engineer"

poor_fit_if_position_requires_deep_specialization_in:
  - "ML research"
  - "cybersecurity"
  - "embedded engineering"
  - "database internals"
  - "large-scale SRE"
  - "kernel or network engineering"