Agentic AI Architect

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.