Overview :

We are seeking an exceptionally skilled and experienced Generative AI Architect to lead the design, development, and deployment of cutting-edge Generative AI solutions. This role requires a blend of deep machine learning and natural language processing expertise with hands-on architectural experience in building scalable, production-grade LLM applications. The ideal candidate will be a technical leader who can drive innovation, define best practices, and mentor engineering teams in the evolving GenAI space.

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 to 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.