Senior ai engineer – generative ai (aws bedrock & agentic systems)

Role Title: Senior AI Engineer – Generative AI (AWS Bedrock & Agentic Systems)
Experience: 7–12 years
Location: India (Remote)
Notice Period: Immediate to 15 days only
Role Summary
We are looking for Senior AI Engineers with strong hands‑on expertise in Generative AI on AWS , specifically AWS Bedrock and agent-based AI architectures. The role involves designing and building production‑grade AI agents, RAG systems, and orchestration workflows that integrate with enterprise applications.
Key Responsibilities
Designed and developed Generative AI applications using AWS Bedrock , integrating foundation models such as Claude, LLa MA, Titan , etc.
Built agentic AI systems (planner–executor, tool‑calling agents, multi-agent workflows) to automate complex business processes
Implemented RAG pipelines using vector databases (Open Search, Pinecone, FAISS, etc.) and enterprise data sources
Developed prompt engineering strategies , guardrails, and evaluation frameworks for accuracy, safety, and cost optimization
Integrated AI agents with AWS services such as Lambda, API Gateway, Step Functions, Dynamo DB, S3
Ensured security, privacy, and compliance (IAM, data masking, encryption, model access controls)
Optimized inference latency and token usage; monitored production workloads and cost metrics
Collaborated with MLOps/Platform teams to support CI/CD, monitoring, and scalable deployments
Acted as a senior technical contributor, guiding juniors and reviewing Gen AI solution designs
Must‑Have Skills (Non‑Negotiable)
7–12 years of overall software/AI engineering experience
Strong hands‑on experience with AWS Bedrock in real projects (not Po C-only exposure)
Proven experience building agentic AI or tool‑using LLM workflows
Solid experience with Python for Gen AI development
Hands‑on experience with RAG architectures , embeddings, and vector stores
Strong AWS fundamentals: IAM, networking basics, serverless services
Clear understanding of Gen AI risks, hallucination management, prompt controls
Good‑to‑Have Skills
Experience with frameworks like Lang Chain, Llama Index, Semantic Kernel
Exposure to enterprise domains such as BFSI, healthcare, insurance, or pharma
Experience in model evaluation, benchmarking, and observability
Prior experience working on customer-facing or production AI platforms
Mandatory Skills:
Hands‑on AWS Bedrock implementation (real project experience, not learning/demo)
Agentic AI / LLM agents / tool‑calling workflows
RAG architectures + vector databases
Strong Python background