Architect (AIML)

Title : AIML GENAI Architect Years of Exp : 10 Years Location : Pune/ Indore/ Kolkata/ BangaloreNP : Immediate JoinerKey ResponsibilitiesDesign and lead the implementation of end-to-end, high-performance Generative AI architectures, focusing on scalability, cost-efficiency, and resilience.Architect and optimize Retrieval-Augmented Generation (RAG) pipelines for enterprise-specific knowledge bases, including vector store integration, indexing, and retrieval optimization.Design and implement complex, multi-step Agentic AI systems and sophisticated conversational flows using frameworks like LangChain and LangGraph.Establish robust MLOps and GenAI-Ops practices, integrating observability and evaluation tools such as LangSmith, Phoenix, or Langfuse to monitor model performance, latency, cost, and drift in production.Define and implement LLM evaluation methodologies, utilizing tools like Ragas to quantitatively assess the quality (e.g., faithfulness, answer relevance, context adherence) of RAG and Agentic AI applications.Continuously evaluate new LLMs, foundation models, open-source frameworks, and emerging GenAI technologies to recommend and integrate the best-fit solutions.Work closely with Data Scientists, ML Engineers, and business stakeholders to translate complex business problems into technical AI/ML roadmaps and provide technical guidance to development teams.Leverage a strong background in ML/NLP to advise on and implement techniques for prompt engineering, model fine-tuning (e.g., LoRA, QLoRA), and advanced text processing.Be part of Technical Presales, Proposals and RFP response teams.QualificationsBE/BTech with 10-15 years of progressive experience in software engineering, data science, or ML engineering roles.Minimum of 5 years of dedicated experience in developing and deploying Artificial Intelligence and Machine Learning solutions.Mandatory Technical SkillsProven experience architecting and shipping production-grade GenAI applications.Deep, hands-on expertise in designing and optimizing Retrieval-Augmented Generation (RAG) systems.Expert-level proficiency with LangChain and LangGraph for building complex LLM chains, tools, and stateful agent workflows.Demonstrated experience in building and deploying multi-tool, reasoning-based Agentic AI systems.Hands-on experience with GenAI-specific observability and evaluation platforms like LangSmith, Phoenix, or Langfuse.Practical knowledge and experience using LLM evaluation frameworks, specifically Ragas, for quality assessment.High proficiency in Python and relevant data science libraries (e.g., PyTorch, TensorFlow, Hugging Face).Experience with cloud platforms (AWS, Azure, or GCP) and MLOps tools (e.g., Kubernetes, Docker) for scalable model deployment.Soft SkillsExcellent written and verbal communication skills, with the ability to clearly articulate complex technical concepts to both technical and non-technical audiences.Demonstrated ability to lead technical initiatives, influence architectural decisions, and mentor junior and senior engineers.