Generative AI/ Document Intelligence Engineer

Job description

**Key Skills:** **1) 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 / multiagent architectures** and workflow-based AI systems - Ability to adapt or fine-tune models for **accuracy, confidence scoring, and explainability** **2) 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) **3) 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 **4) AIAugmented 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 **5) 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) **6) 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 **7) 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 **8) Cost & Performance Optimization:** - 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** **9) 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** **10) 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**