Document Intelligence Engineer

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:
  • Object storage (e.g. S3)
  • Serverless compute (e.g. Lambda)
  • 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:
  • Monitoring and alerting
  • Reliability and resilience
  • Scalability and performance
  • 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:
  • Governance and auditability
  • Explainability and transparency
  • Bias and fairness considerations
  • 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:
  • Model selection and tiering
  • Caching and reuse strategies
  • 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