GenAI Developer – LLMs, RAG

7 days ago

Cochin, Kerala, India Milestone Technologies, Inc. Full time ₹ 12,00,000 - ₹ 36,00,000 per year

GenAI Developer – LLMs, RAG & Knowledge Graphs (Neo4j)

Experience: 5–8 years total (2+ years in Generative AI)
Location: Kochi
Job Type: Full-time

Role Overview

We're looking for a hands-on GenAI Developer to design, build, and productionize LLM-powered applications—retrieval-augmented generation (RAG), conversational agents, document understanding, and decision support. You'll combine AWS Bedrock/SageMaker with open-source stacks and integrate a Neo4j knowledge graph to deliver reliable, explainable AI in a product environment.

What You'll Do

  • Ship end-to-end GenAI features: data ingestion → chunking/embedding → RAG pipelines → prompt orchestration → evals → deployment → monitoring.
  • Build robust RAG systems: hybrid retrieval (vector + keyword), query re-writing, reranking, citations, and grounding on enterprise data.
  • Model orchestration on AWS: Bedrock model selection (Claude, Llama, etc.), prompt flows, guardrails, and cost/perf tuning; SageMaker for custom training/fine-tuning (LoRA/QLoRA/PEFT).
  • Knowledge Graph (Neo4j): design schema/ontologies, build entity/relation extractors, create KG-augmented RAG, and run graph reasoning with Cypher & GDS.
  • Eval & quality: define automatic evals (faithfulness, answer relevance, toxicity, latency), A/B tests, and red-teaming; maintain prompt and dataset versioning.
  • Ops & reliability: implement LLMOps (observability, tracing, guardrails, rate/cost controls), CI/CD, IaC, and runtime monitoring.
  • APIs & apps: expose services via REST/GraphQL; build lightweight UIs or chat backends, integrate auth, and log interactions for continuous improvement.
  • Security & compliance: PII handling, prompt injection defense, output filtering, and enterprise IAM hygiene.

Core Skills & Tech Stack

Languages & Frameworks

  • Python (FastAPI), (nice-to-have), SQL
  • GenAI frameworks: LangChain / LlamaIndex / Haystack
  • Prompt tooling & observability: Langfuse, Weights & Biases (or equivalents)

LLMs & Modeling

  • Retrieval & embeddings: Bedrock embeddings, sentence-transformers, Cohere/HF embeddings
  • Fine-tuning/distillation: LoRA/QLoRA/PEFT, bitsandbytes, Hugging Face ecosystem
  • Rerankers & rewriters: cross-encoders, T5/FLAN or similar
  • Safety/guardrails: content filters, groundedness checks, regex/policy engines

AWS (Focus)

  • Bedrock (model access, guardrails), SageMaker (training, endpoints), ECR/ECS/EKS, Lambda, API Gateway, S3, CloudWatch, Step Functions
  • OpenSearch / Kendra for retrieval; RDS/ Aurora / DynamoDB for metadata/state
  • IAM, Secrets Manager, VPC for security & networking

Knowledge Graphs

  • Neo4j (schema/ontology design, Cypher, APOC), Graph Data Science (GDS)
  • Entity/relation extraction pipelines; KG-RAG patterns; graph-based reasoning and path explanations

Vector/Search

  • OpenSearch vector, pgvector, Milvus, or Pinecone (any 1–2 strongly)

MLOps / LLMOps

  • MLflow or SageMaker Model Registry; Docker; CI/CD (GitHub Actions/GitLab)
  • Tracing/telemetry, dataset & prompt versioning, cost/latency dashboards

Qualifications

  • Bachelor's/Master's in CS/EE/Math or equivalent practical experience
  • 5–8 years building data/ML systems; 2+ years specifically with LLMs/GenAI
  • Proven production deployments on AWS and experience with Neo4j in real use cases

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