Senior AI Engineer – Generative AI (AWS Bedrock & Agentic Systems)

Job description

**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