Tech Lead / Senior Python Engineer (AI, RAG & Knowledge Graphs)

We are looking for a highly versatile senior engineer who can operate as an architect, rapid prototyper, and technical driver for an AI-heavy document intelligence platform. The role focuses on building the core AI engine, designing knowledge-graph and RAG pipelines, integrating with Microsoft 365, and shaping the technical foundation that will later scale into a full enterprise solution.

This is a hybrid role for a senior engineer who can independently deliver the first version of the system, validate the architecture, and later grow and lead a team around it.

About the Project

The client is a global solar energy developer. The goal is to build an AI-powered Corporate Knowledge layer capable of:

• ingesting project documentation,
• extracting structured insights,
• running checklist-based compliance screening (ESG, technical, regulatory),

The solution uses Azure, Azure OpenAI, Memgraph, LlamaIndex, and Microsoft Graph API.

Your responsibility is to architect and build the AI core, implement ingestion and RAG logic, integrate with Microsoft 365, and deliver the POC end-to-end.

Architecture, Engineering & Solution Design

• Own the architecture of the AI backend
• Design the Python backend (FastAPI) and integrations with Azure services
• Model and implement the knowledge graph (Memgraph)
• Architect Graph-RAG pipelines
• Define interfaces between backend, Teams bot, and React dashboard
• Deliver fast POCs and evolve them into scalable designs

Full-Stack Development

• Build the backend (FastAPI, async processing, orchestrations)
• Integrate with Azure Blob, SharePoint and Microsoft Graph API
• Implement lightweight UI components in React for POC dashboards
• Set up CI/CD (GitHub Actions/Azure Pipelines)
• Deploy services using Docker and Azure Container Apps

AI & Knowledge-Graph Engineering

• Integrate Azure OpenAI models (LLMs + embeddings)
• Build document-processing pipelines (Docling, PyPDF, Office parsers)
• Implement retrieval and indexing with LlamaIndex
• Design and maintain the knowledge graph schema (documents, facts, requirements, checklist statuses)
• Implement reasoning tools for the Screening Agent (search_documents, query_graph, update_status)
• Optimise prompts, context construction, and retrieval performance
• Analyse model errors and iteratively improve pipelines

POC Leadership & Team Enablement

• Lead the end-to-end delivery of the POC
• Present technical findings and results to business and IT stakeholders
• Define the transition path from POC to production architecture
• Identify hiring needs for backend, frontend, ML, and QA roles
• Mentor future engineers and set engineering standards

Required Qualifications

Experience

• 6+ years of professional backend engineering experience (Python)
• 3+ years as a senior engineer or tech lead
• Strong track record of building end-to-end applications (backend + basic frontend + integrations)
• Solid experience with AI applications (LLMs, RAG, embeddings, document extraction)
• English proficiency (Upper-Intermediate+) for direct stakeholder communication

Technical Skills

• Expert-level Python (FastAPI, async, architecture patterns, typing)
• Experience building AI-powered systems with Azure OpenAI
• Strong understanding of RAG, embeddings, retrieval and prompt-engineering
• Experience with knowledge graphs (Memgraph, Neo4j, or similar)
• Good knowledge of Azure services (Blob Storage, AAD, Container Apps, Functions)
• Solid Docker & CI/CD skills
• Ability to build simple UIs in React when needed

Nice to Have

• Experience with Microsoft Graph API, SharePoint and Teams Bot Framework
• Graph-RAG or knowledge-graph-centric architectures
• Experience with LlamaIndex in production
• Understanding of enterprise security patterns (OAuth2, SSO)

Ideal Candidate Profile

• The ideal candidate combines:
• deep engineering and architectural expertise,
• ability to prototype quickly under uncertainty,
• confidence across the full stack,
• excellent communication with both business and technical audiences,
• leadership qualities to define technical direction and scale the team,
• curiosity and adaptability in the rapidly evolving AI landscape.