Generative AI Engineer

Position : GenAI Engineer

Location : Gurugram

Type : Full-time (Direct Hire, Client Payroll)

Number of positions : 6

Exp Level : 4- 7 Years

Immediate Joiners, 7-10 days Max (Serving Notice)

Previous MNC exp

Job Description :

- Strong proficiency in Python (async, typing, packaging, unit testing); solid grounding in data structures and algorithms

- Hands on with AWS-specifically Amazon Bedrock, SageMaker, S3, Lambda, Step Functions, CloudWatch, IAM; vector search - pgvector, OpenSearch vector

- Working knowledge of LangChain/LangGraph, RAG architectures, prompt design & evaluation; familiarity with guardrails (policy, PII/Secrets redaction), HITL patterns

- Experience with ETL/data processing (Pandas / Spark) and NoSQL/SQL stores (DynamoDB/Postgres)

- Proficiency with Git, CI/CD (GitHub Actions/Jenkins), Docker, and basic Kubernetes concepts

- Understanding of microservices and event driven integrations (queues, webhooks); observability (logging, metrics, tracing)

- Bonus : ReactJS for full stack prototypes; Copilot usage patterns for engineering productivity; document pipelines

What you must bring (Experience) :

- 4- 7 years of software engineering experience GenAi services focused, including 2+ years building or integrating GenAI/ML solutions

- Bachelor's degree in Engineering/Computer Science (or equivalent)

- Practical understanding of IT + business concepts and how AI features translate to measurable outcomes

Responsibilities :

What will you do :

- Design and develop AI powered backend systems and services that leverage Bedrock (model selection, orchestration), RAG pipelines (indexing, retrieval, evaluation), and S3 content sources

- Build secure APIs that integrate AI features into internal apps; partner with UI teams on full stack delivery

- Implement prompt engineering, evaluation harnesses, safety filters, and HITL review workflows; measure quality (precision, hallucination rate, latency, cost)

- Create ETL/ingestion jobs and embeddings pipelines; optimize chunking, metadata, and retrieval performance

- Productionize with CI/CD, IaC, logging/monitoring, and cost/latency optimization; contribute to runbooks and SLOs

- Collaborate with product owners, domain SMEs, data engineering, security, and compliance to align on value, controls, and go live readiness

- Champion reusability by packaging patterns (RAG, agents, evaluation) as internal accelerators and documenting best practices

Nice to have / Good to have :

- Experience with agentic workflows (task decomposition, tools, function calling) and evaluation frameworks

- Knowledge of SageMaker model endpoints, fine tuning/parameter efficient tuning, and feature stores

- Familiarity with pgvector/OpenSearch, prompt/test case libraries, cost observability, and Copilot productivity dashboards