Generative AI Engineer

Job description:

KEY RESPONSIBILITIES

• · Design and own multi-stage ingestion pipelines — handling HTML, PDF, and image sources with layout parsing, metadata extraction, and vector storage
• · Architect RAG systems with hybrid search (BM25 + semantic), document versioning, and cross-reference resolution
• · Build production-grade FastAPI services with typed response envelopes, OpenAPI compliance, and Langfuse tracing integration
• · Engineer prompt systems — structured prompts, prompt versioning, few-shot strategies, and judge-based evaluation
• · Integrate and manage LLM routing via LiteLLM: model fallback, cost control, and per-route configuration
• · Design agentic workflows using LangGraph: multi-step retrieval, tool use, and conditional branching
• · Build and maintain knowledge graphs in NebulaGraph / Neo4j — entity extraction, relationship modelling, and domain ontology alignment
• · Implement graph-augmented retrieval (GraphRAG) — combining vector search with graph traversal to surface contextually connected information beyond chunk-level retrieval
• · Own entity linking and co-reference resolution pipelines that connect ingested documents to graph nodes
• · Lead RAG evaluation initiatives — define metrics, build eval datasets, and run regression cycles
• · Drive observability standards — tracing, cost attribution, and latency profiling via Langfuse
• · Collaborate on K8s deployment patterns for AI services: resource limits, GPU scheduling, and health probes
• · Mentor junior developers and conduct code and prompt reviews

REQUIRED SKILLS

• · Python (5+ years) — async, concurrency patterns, production packaging
• · Deep understanding of RAG — hybrid retrieval, reranking, chunking strategies, embedding model selection
• · FastAPI — dependency injection, middleware, background tasks, async patterns
• · Prompt engineering — structured prompting, chain-of-thought, evaluation-driven iteration
• · LLM API integration — OpenAI-compatible APIs, AWS Bedrock, or similar
• · Vector DB expertise — Weaviate or equivalent: schema design, indexing, and filtering
• · Document parsing at scale — Docling, layout models, VLM-based extraction from PDFs, HTML, and images
• · Graph DB — NebulaGraph or Neo4j: schema design, Cypher / nGQL queries, knowledge graph construction
• · Observability mindset — tracing, evaluation loops, cost-aware system design