Location: Pune, Gurgaon, Mohali, Panchkula, Dehradun, Bangalore, Mumbai, Chennai

Check Points:

1. VERY STRONG communication skills
2. Women in general not preferred as the work timings are 4 pm to 1 am, but if women candidates are okay with this shift they can apply.
3. No cab provision to employee from compan

Required Skills

- Solid hands-on experience building AI/LLM-based RAG applications and AI agents.
- Experience with LangChain, LangGraph, AutoGen, CrewAI, Semantic Kernel, or similar agent frameworks.
- Experience with MCP servers and MCP client integrations.
- Proficiency in Python and backend/API development.
- Experience building services with FastAPI.
- Strong understanding of LLMs, AI agents, RAG, embeddings, rerankers, vector databases, and retrieval systems.
- Experience with prompt engineering, tool calling, structured outputs, and agent orchestration.
- Experience with foundation models such as GPT, Claude, Llama, Gemini, Amazon Nova, or equivalent.
- Experience with vector databases such as Pinecone, ChromaDB, Qdrant, Weaviate, or similar technologies.
- Experience with object/blob storage such as Amazon S3.
- Experience with search and vector search systems such as Amazon OpenSearch.
- Experience with semantic search, hybrid search, BM25, and retrieval optimization techniques.
- Experience with cross-encoders, bi-encoders, reranking models, and embedding models.
- Experience with AI observability and evaluation tools.
- Experience with Git commands.
- Experience using GitHub and GitHub Actions for version control, CI/CD, and collaboration.
- Experience with SQL and database design.
- Knowledge of cloud security, authentication, authorization, and secure AI application development.

Responsibilities

- Build AI/LLM applications on AWS using AWS services.
- Prompt engineering and evaluation frameworks
- Build AI agents using frameworks such as LangChain, LangGraph, Amazon Bedrock, AutoGen, or similar tools.




- Develop backend services and APIs using FastAPI.
- Integrate AWS services such as Amazon Bedrock, S3, Lambda, ECS/EKS, OpenSearch, and AWS database services.
- Work with foundation models such as OpenAI GPT, Anthropic Claude, Meta Llama, Amazon Nova, Gemini, or similar models.
- Design and implement AI agent architectures, multi-agent systems, and tool-calling workflows.
- Implement RAG pipelines using blob/object storage, vector search, embeddings, reranking, and document retrieval workflows.
- Build semantic search, hybrid search, and knowledge retrieval systems.
- Implement prompt engineering, prompt management, and LLM evaluation strategies.
- Develop structured output and function/tool-calling capabilities.
- Implement streaming AI responses using SSE or WebSockets.
- Implement AI application monitoring, observability, tracing, and evaluation frameworks.
- Design guardrails, content filtering, hallucination mitigation, and responsible AI controls.
- Optimize AI applications for latency, scalability, reliability, and cost.
- Work with application teams to move AI prototypes into reliable production environments.
- Implement cloud security best practices.
- Collaborate with DevOps and platform teams to deploy, monitor, and scale AIworkloads.

Nice to Have

- Experience with Node.js and TypeScript.
- Experience with Amazon Bedrock, SageMaker, Azure OpenAI, OpenAI APIs, Anthropic APIs, or Google Vertex AI.
- Experience containerizing and deploying applications using Docker and Kubernetes.
- Experience with AI observability platforms such as Langfuse, Arize Phoenix, Helicone, or similar tools.
- Experience with model evaluation frameworks such as Ragas, DeepEval, LangSmith, or similar tools.
- Experience building multi-agent systems and autonomous workflows.
- Knowledge of enterprise security, compliance, governance, and cost optimization practices.
- Experience deploying production-grade AI solutions on AWS.
- Familiarity with MLOps, LLMOps, CI/CD, and Infrastructure as Code (Terraform, CloudFormation).

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