Core AI & ML Skills:

- Hands-on experience building GenAI solutions (LLMs, RAG pipelines, embeddings, semantic search)

- Practical use of OCR and document intelligence techniques across unstructured data (PDFs, images, scanned forms)

- Strong understanding of NLP concepts (entity extraction, classification, keyword detection)

- Experience with agentic / multi agent architectures and workflow-based AI systems

- Ability to adapt or fine-tune models for accuracy, confidence scoring, and explainability

Architecture & System Design:

- Proven ability to design end-to-end AI platforms, beyond proof-of-concepts

- Experience with large-scale document pipelines (ingestion - processing - indexing - retrieval)

- Strong knowledge of RAG vs alternative architectures (hybrid search, knowledge graphs, semantic indexing)

- Experience with event-driven and serverless patterns for scalable processing

- Ability to reason about trade-offs (accuracy vs cost, latency vs scale, complexity vs maintainability)

Cloud & Platform Engineering:

- Strong experience in at least one major cloud platform (AWS preferred)

- Familiarity with:

1. Object storage (e.g. S3)

2. Serverless compute (e.g. Lambda)

3. Managed AI/ML and OCR services

- Infrastructure-as-Code mindset (e.g. Terraform or equivalent)

- Ability to design cloud-agnostic solutions where required

AI Augmented Engineering (Prompt Coding & AI Pairing):

- Strong ability to use prompt engineering / prompt coding to generate, debug, and accelerate production-quality code

- Demonstrated capability to pair-program effectively with AI tools, iterating prompts and validating outputs

- Ability to apply judgement on when to rely on vs avoid AI-generated code, especially for security or critical logic

- Experience integrating AI into engineering workflows (test generation, documentation, code reviews)

- Maintains strong engineering fundamentals and code quality standards while leveraging AI as a productivity multiplier

MCP AI Integration (Model, Context, Platform Integration):

- Experience integrating AI models into enterprise systems using API-first and service-oriented architectures

- Ability to design model orchestration layers that connect LLMs, tools, data sources, and workflows (e.g. retrieval systems, APIs, event streams)

- Strong understanding of context injection patterns (prompt construction, metadata enrichment, grounding, tool usage)

- Experience building scalable integration pipelines between AI services and enterprise platforms (e.g. ECM systems, data lakes, APIs)

- Awareness of security, governance, and compliance controls in AI integration (PII handling, access control, audit logging, isolation boundaries)

Production Readiness & Operations:

- Clear understanding of production-ready AI systems, including:

1. Monitoring and alerting

2. Reliability and resilience

3. Scalability and performance

4. Observability and runtime support

- Experience integrating into CI/CD and DevSecOps pipelines

- Awareness of security scanning, vulnerability management, and secure deployments

Responsible AI & Risk Awareness:

- Strong grounding in responsible AI principles, including:

1. Governance and auditability

2. Explainability and transparency

3. Bias and fairness considerations

4. Human-in-the-loop controls

- Experience working in regulated or high-risk environments

- Ability to design solutions with compliance and audit requirements in mind

Cost & Performance Optimisation:

- Ability to design for cost-efficient AI usage, including:

1. Model selection and tiering

2. Caching and reuse strategies

3. Routing tasks to appropriate model complexity

- Awareness of token usage, OCR costs, and scaling cost drivers

- Experience implementing logging, metrics, and cost observability

Engineering & Delivery Skills:

- Strong Python development skills and familiarity with AI/ML ecosystems

- Ability to deliver end-to-end solutions (POC - MVP - production)

- Experience working in cross-functional engineering teams

- Comfortable operating as a senior individual contributor with architectural influence

Communication & Collaboration:

- Ability to explain complex AI systems to technical and non-technical stakeholders

- Comfortable collaborating with platform, security, and compliance teams

- Balances hands-on delivery with design leadership