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Job Description
Structured overview of role & requirementsAbout This Role
Design, develop, and deploy autonomous Agentic AI systems capable of reasoning, planning, memory retention, and task execution using multi-agent orchestration frameworks like AutoGen.
Build, optimize, and integrate RAG-based AI models and workflows including vector databases, semantic/hybrid search for enterprise AI assistants and copilots.
Support end-to-end AI/ML workflows from implementation through deployment, validation, and monitoring in cloud-based, production enterprise environments.
Minimum Requirements
3 to 5 years of hands-on experience in AI/ML and GenAI engineering.
Strong proficiency in Python and experience building AI/ML/DL models using LLMs and related frameworks.
Experience with RAG architectures, LLMs, vector databases, multi-agent orchestration (AutoGen mandatory, Semantic Kernel beneficial), and prompt engineering.
Production deployment experience with AI/ML/GenAI applications including containerization and CI/CD; familiarity with SQL databases and enterprise data sources.
Ideal Candidate Profile
Experienced in building and orchestrating Agentic AI agents with capabilities in reasoning, planning, and tool usage for solving complex enterprise problems.
Skilled in developing scalable, production-ready AI systems integrating structured and unstructured enterprise knowledge.
Comfortable working in cloud and enterprise environments handling large-scale data pipeline development and deployment.