Experience Level: 8+ years (minimum 2–3 years in ML, Gen AI and Multi-Agent Systems)

Key Responsibilities:
Architect and implement agentic AI systems using up-to-date LLM orchestration frameworks (LangChain, CrewAI, AutoGen, etc.).
Design multi-agent collaboration models including planner-solver, autonomous teams, and goal decomposition agents.
Build reusable tooling, APIs, and memory architectures for agent interaction, coordination, and context persistence.
Lead hands-on development and deployment of GenAI applications (e.g., assistants, copilots, decision support).
Evaluate and integrate LLMs (OpenAI, Claude, Mistral, LLaMA, etc.), vector databases (Pinecone, Weaviate, FAISS), and retrieval systems (RAG).
Optimize agent performance for real-time environments, reliability, scalability, and ethical constraints.
Guide teams in adopting agent frameworks, best practices, prompt engineering, and model fine-tuning.
Collaborate with stakeholders to translate business requirements into technical solutions using agent-based paradigms.




Continuously monitor trends in multi-agent systems, cognitive architectures, and open-source AI frameworks.

Must-Have Skills:
2+ years of hands-on experience in agentic AI / multi-agent systems.
Proficiency with LangChain, Langraph, CrewAI, AutoGen, Haystack, or equivalent frameworks.
Solid background in Python and experience with prompt engineering, tools integration, and chaining logic.
Solid understanding of LLM APIs, RAG, vector stores, tool use, and memory architectures.
Hands-on experience with open-source and commercial LLMs (e.g., GPT-4, Claude, Gemini, Mistral).
Experience deploying AI agents in cloud-native environments (AWS, GCP, Azure).
Ability to lead architectural discussions, PoCs, and hands-on development in fast-paced environments.
Model-cost profiling and budgeting (API call minimization, batch vs. streaming)
Latency tuning for real-time agents, Autoscaling strategies.

Positive-to-Have Skills:

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