DL
Agentic AI+AWS
Diverse Lynx India
- 🇮🇳 India
- Hybrid
- 13 hours ago
- AI
- RAG
- LangGraph
- CrewAI
- AutoGen
- MCP
- Model Context Protocol
- AWS
- GCP
- Azure
- Python
- LLM APIs
13 hours ago
Description:
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MAH | PUNE | ||
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TELANGANA | HYDERABAD | ||
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ORS | BHUBANESWAR | ||
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4+ | ||
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• Explore & research: track the agentic AI landscape, evaluate new models, tools, and ideas, and run quick experiments to prove what is worth pursuing. • Develop AI agents: design and ship agents that reason, plan, use tools/APIs, and handle memory and retrieval (RAG). • Orchestrate multi-agent systems: build systems where specialized agents collaborate, delegate, and hand off tasks to solve complex problems. • Build with agent frameworks: implement agents using frameworks such as Strands, LangGraph, CrewAI, or AutoGen, applying the right patterns for each use case. • Implement MCP & integrations: connect agents to tools, data, and services through the Model Context Protocol (MCP) and other AI integration technologies and APIs. • Deploy on the cloud: package, deploy, and run agents on cloud platforms (AWS, GCP, or Azure) with scalability, security, and cost in mind. • Add observability: instrument agents with logging, tracing, and monitoring so behavior can be measured, debugged, and improved in production. • Productionize & evaluate : Define quality, success, latency, and cost metrics, build eval harnesses, and optimize for reliability and scale. • Ensure safety: apply guardrails, tool permissions, and human-in-the-loop checks for responsible agent behavior. • Collaborate & enable: partner with product and domain teams, document decisions, and raise the team's overall agentic-AI fluency. |
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• Have good hands-on experience shipping LLM-powered or AI agent applications. • Strong Python skills and solid software-engineering fundamentals. • Hands-on experience on more than one agent framework such as Strands, LangGraph, CrewAI, or AutoGen, and with modern LLM APIs. • Working knowledge of prompt engineering, tool/function calling, RAG, embeddings/vector stores, and agent memory. • Experience with cloud platforms (AWS, GCP, or Azure) for deploying and running AI workloads. • Familiarity with MCP implementation or other AI integration technologies for connecting agents to tools and data. • Experience with observability and tracing for AI/LLM systems. • A fast experimentation mindset and clear communication with technical and non-technical audiences. |
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IT | ||
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3-6 yrs | ||
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Face to Face | ||
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Hybrid | ||
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Hybrid Work Model | ||
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Others |
Agentic AI+AWS · Diverse Lynx India