Gen AI​/Agentic Engineer

Job Summary

We are looking for a GenAI / Agentic Engineer to design, build, and deploy LLM-powered applications on AWS. This role is focused on real production engineering‑APIs, RAG pipelines, agent workflows, evaluation, deployment, monitoring, and performance/cost tuning.

Job Title:

Gen AI / Agentic Engineer

Location:

Remote

Type: W2 Contract

• Build and maintain LLM‑powered backend services using Python and FastAPI (chat, search, summarization, Q&A).
• Design and implement RAG pipelines end‑to‑end: ingestion, parsing, chunking, embeddings, indexing, retrieval, reranking, and grounded responses.
• Develop agentic workflows for multi‑step automation (tool calling, orchestration, state/memory, retries, audit logs).
• Deploy and support GenAI workloads on AWS using ECS/Lambda, S3, SQS, Dynamo
  
  DB/RDS, Open Search (or vector store), and related services.
• Implement security and governance controls: auth, authorization, secrets, encryption, PII handling, and prompt‑injection defenses.
• Build evaluation and monitoring for quality, hallucination reduction, latency, and cost (test sets, regression checks, dashboards, alerts).
• Work across full SDLC: design docs, estimates, coding, code reviews, CI/CD, testing, release, and production support.
• Communicate architecture decisions clearly and explain tradeoffs (accuracy vs latency vs cost) to stakeholders.

• 10+ years overall IT experience with backend/API engineering and cloud deployments
• 2+ years hands‑on GenAI/LLM experience delivering real features (not just demos)
• 6+ years strong Python (core Python, clean coding, debugging, packaging)
• Experience with asyncio and concurrency (threads/async), plus profiling and performance tuning
• Comfortable with stateful/long‑running workflows: transaction handling, retries, idempotency, and failure recovery
• 5+ years building REST APIs / microservices, strong API design and error handling
• 5+ years with FastAPI (or similar) including middleware, dependency injection, background tasks
• Experience implementing auth/security using JWT/OAuth, RBAC, secure configuration, secrets handling
• Strong testing discipline using pytest (unit/integration tests, mocks, API contract testing)
• Proven experience building RAG systems end‑to‑end: chunking strategies, embeddings, retrieval tuning, reranking, grounding/citations
• Hands‑on with RAG optimization: hybrid retrieval, metadata filters, top‑k tuning, chunk tuning, reranking strategies
• Experience with agentic patterns: tool calling, orchestration, memory/state, structured outputs, audit trails
• Experience implementing guardrails: output schema enforcement (JSON), refusal handling, safety filters, prompt‑injection defenses, PII masking
• 5+ years AWS experience using ECS/Lambda, S3, SQS, Dynamo
  
  DB/RDS (and related services)
• Strong AWS security fundamentals: IAM, KMS, Secrets Manager, Cloud Watch logs/metrics/alarms
• Experience deploying LLM workloads via Amazon Bedrock (preferred) or Sage Maker
• Strong system design: scalability, caching, rate limiting, queues, resilience/failure handling
• Ability to clearly explain GenAI architecture decisions and tradeoffs across accuracy/latency/cost

• Lang Chain / Lang Graph / Llama Index (any)
• Open Search vector search or vector DB experience (Pinecone/Weaviate/FAISS, etc.)
• Docker, Terraform/CDK, CI/CD (Git Hub Actions/Jenkins)
• Experience in regulated environments (finance/healthcare/telecom) with governance controls