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, LLaMA, 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 (OpenSearch, 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, DynamoDB, 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 GenAI 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 PoC-only exposure) Proven experience building agentic AI or tool‑using LLM workflows Solid experience with Python for GenAI development Hands‑on experience with RAG architectures , embeddings, and vector stores Strong AWS fundamentals: IAM, networking basics, serverless services Clear understanding of GenAI risks, hallucination management, prompt controls Good‑to‑Have Skills Experience with frameworks like LangChain, LlamaIndex, 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