Key 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.
Qualifications
- 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 Machine Learning 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.
Soft Skills
- Excellent 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.
