Agentic AI Architect

1 week ago

Bengaluru Hyderabad New Delhi, India Excelra Full time ₹ 20,00,000 - ₹ 25,00,000 per year

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.
  • Strong 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."
  • Exposure to Autonomous AI agents (AutoGPT, BabyAGI, CAMEL, MetaGPT).
  • Understanding of LLM fine-tuning, adapters, and RLHF.
  • Experience with agent simulation, environment modeling, or reinforcement learning is a plus.
  • Familiarity with compliance, privacy, and safety in GenAI deployments.
  • Prior experience in building domain-specific agents (Lifescience, healthcare, Pharma).

Key Responsibilities:

  • Architect and implement agentic AI systems using modern 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.

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