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Artificial Intelligence Architect - LLM/RAG Job Opening In India, India – Now Hiring Skyleaf


Job description

<p><p><b>Job Title : </b> Agentic AI Architect<br/><br/><b>Experience Level : </b> 10+ years (minimum 23 years in ML, Gen AI and Multi-Agent Systems)<br/><br/><b>Key Responsibilities : </b></p><p><p><b><br/></b></p>- Architect and implement agentic AI systems using modern LLM orchestration frameworks (LangChain, CrewAI, AutoGen, etc.).<br/><br/></p><p>- Design multi-agent collaboration models including planner-solver, autonomous teams, and goal decomposition agents.<br/><br/></p><p>- Build reusable tooling, APIs, and memory architectures for agent interaction, coordination, and context persistence.<br/><br/></p><p>- Lead hands-on development and deployment of GenAI applications (e.g., assistants, copilots, decision support).<br/><br/></p><p>- Evaluate and integrate LLMs (OpenAI, Claude, Mistral, LLaMA, etc.), vector databases (Pinecone, Weaviate, FAISS), and retrieval systems (RAG).<br/><br/></p><p>- Optimize agent performance for real-time environments, reliability, scalability, and ethical constraints.<br/><br/></p><p>- Guide teams in adopting agent frameworks, best practices, prompt engineering, and model fine-tuning.<br/><br/></p><p>- Collaborate with stakeholders to translate business requirements into technical solutions using agent-based paradigms.<br/><br/></p><p>- Continuously monitor trends in multi-agent systems, cognitive architectures, and open-source AI frameworks.<br/><br/><b>Must-Have Skills : </b><br/><br/></p><p>- 2+ years of hands-on experience in agentic AI / multi-agent systems.<br/><br/></p><p>- Proficiency with LangChain, Langraph, CrewAI, AutoGen, Haystack, or equivalent frameworks.<br/><br/></p><p>- Strong background in Python and experience with prompt engineering, tools integration, and chaining logic.<br/><br/></p><p>- Solid understanding of LLM APIs, RAG, vector stores, tool use, and memory architectures.<br/><br/></p><p>- Hands-on experience with open-source and commercial LLMs (e.g., GPT-4, Claude, Gemini, Mistral).<br/><br/></p><p>- Experience deploying AI agents in cloud-native environments (AWS, GCP, Azure).<br/><br/></p><p>- Ability to lead architectural discussions, PoCs, and hands-on development in fast-paced environments.<br/><br/></p><p>- Model-cost profiling and budgeting (API call minimization, batch vs.

streaming)<br/><br/></p><p>- Latency tuning for real-time agents, Autoscaling strategies.<br/><br/><b>Good-to-Have Skills : </b><br/><br/></p><p>- Exposure to Autonomous AI agents (AutoGPT, BabyAGI, CAMEL, MetaGPT).<br/><br/></p><p>- Understanding of LLM fine-tuning, adapters, and RLHF.<br/><br/></p><p>- Experience with agent simulation, environment modelling, or reinforcement learning is a plus.<br/><br/></p><p>- Familiarity with compliance, privacy, and safety in GenAI deployments.<br/><br/></p><p>- Prior experience in building domain-specific agents (Lifescience, healthcare, Pharma).</p><br/></p> (ref:hirist.tech)

Required Skill Profession

Computer Occupations


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