Gen AI Agentic Engineer

#W2 Role

Job Title: Gen AI / Agentic Engineer

Location: Remote
Type: W2 Contract
Experience: 10 years overall IT 2 years GenAI/LLM

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.

Responsibilities

• 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 DynamoDB/RDS OpenSearch (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.

Required Skills (Point-Based)

• 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 DynamoDB/RDS (and related services)
• Strong AWS security fundamentals: IAM KMS Secrets Manager CloudWatch logs/metrics/alarms
• Experience deploying LLM workloads via Amazon Bedrock (preferred) or SageMaker
• Strong system design: scalability caching rate limiting queues resilience/failure handling
• Ability to clearly explain GenAI architecture decisions and tradeoffs across accuracy/latency/cost

Nice to Have

• LangChain / LangGraph / LlamaIndex (any)
• OpenSearch vector search or vector DB experience (Pinecone/Weaviate/FAISS etc.)
• Docker Terraform/CDK CI/CD (GitHub Actions/Jenkins)
• Experience in regulated environments (finance/healthcare/telecom) with governance controls