Artificial Intelligence Architect - LLM/RAG

Job Title : Agentic AI ArchitectExperience Level : 10+ years (minimum 23 years in ML, Gen AI and Multi-Agent Systems)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.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.Good-to-Have Skills : - Exposure to Autonomous AI agents (AutoGPT, BabyAGI, CAMEL, MetaGPT).- Understanding of LLM fine-tuning, adapters, and RLHF.- Experience with agent simulation, environment modelling, 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). (ref:hirist.tech)