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Metro location, mid-level experience, and high-demand LLM skills create moderate candidate competition.
Role requires specialized LLM, agentic-framework, RAG and vector DB expertise, making cross-industry transferability limited.
Explicit years plus numerous mandatory LLM, agent frameworks, cloud, containerization, and MLOps requirements indicate strict filters.
Design, develop, and deploy multi-agent and agentic AI systems using frameworks like AutoGen, LangGraph, and CrewAI focused on autonomous workflows and generative AI.
Implement agent communication protocols (A2A, MCP) and build intelligent workflow orchestration systems enabling autonomous decision-making and self-healing adaptive workflows.
Deploy and optimize LLM models and generative AI solutions on cloud platforms (AWS Bedrock, SageMaker) integrating RAG architectures, vector DBs, and knowledge graphs.
3-6 years of technical experience including 2 years hands-on in Generative AI and LLM technologies.
1+ years experience building agentic systems, workflow automation, or autonomous AI applications.
Proficiency in Python is required; experience with agentic frameworks, workflow orchestration tools, and cloud-based LLM platforms (AWS) is mandatory.
Work location: Hybrid Bengaluru.
Experienced with multi-agent architectures including memory, planning, and tool-use capabilities and agent evaluation/testing frameworks.
Skilled in ML frameworks (TensorFlow, PyTorch, Hugging Face) with strong knowledge of NLP and deep learning models (BERT, GPT, T5).
Demonstrated ability to design scalable, production-level AI solutions involving MLOps, containerization (Docker, Kubernetes), and CI/CD pipelines in hybrid cloud environments.