Gen AI / Agentic Engineer
Interon IT Solutions
- Chantilly, Virginia, United States
- Chantilly, Virginia, United States
About
#W2 Role Job Title: Gen AI / Agentic Engineer
Location: Remote
Type: W2 Contract
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
#J-18808-Ljbffr
Languages
- English
Notice for Users
This job comes from a TieTalent partner platform. Click "Apply Now" to submit your application directly on their site.