Generative AI Architect

Role & responsibilities

• Design 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.

Preferred candidate profile

• BE/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 MachineLearning solutions.
• Mandatory Technical Skills
• Proven 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.