Key Responsibilities:

- Lead the design and architecture of enterprise AI and Generative AI solutions.
- Define end-to-end architecture for scalable AI applications, platforms, and reusable AI services.
- Translate business requirements and use cases into scalable Data & AI technical solutions.
- Design and implement solutions leveraging LLMs, RAG, AI agents, model orchestration, embeddings, vector search, and knowledge retrieval.
- Evaluate and select appropriate AI models, frameworks, cloud services, databases, and technology stacks.
- Lead the development of full-stack AI applications, integrating AI services with backend systems, APIs, frontend applications, and enterprise platforms.
- Design scalable backend services using Python, REST APIs, microservices, and cloud-native technologies.
- Lead the implementation of RAG pipelines including document ingestion, chunking, embeddings, retrieval, reranking, and response generation.
- Architect and implement solutions using Azure, AWS, and/or GCP.
- Drive production deployment using CI/CD, Docker, Kubernetes, DevOps, MLOps/LLMOps, and cloud-native practices.
- Ensure AI solutions meet enterprise requirements for scalability, security, reliability, performance, observability, governance, and cost optimization.
- Lead technical POCs and drive the transition of successful prototypes into production-grade solutions.
- Provide technical leadership, mentoring, and guidance to AI engineers and cross-functional engineering teams.
- Review architecture, solution designs, technical approaches, and code to ensure engineering quality and scalability.
- Collaborate closely with enterprise architects,



data engineers, software engineers, product teams, and business stakeholders.
- Lead enterprise AI transformation and modernization initiatives and identify opportunities to apply AI and GenAI to business problems.
- Define reusable AI capabilities, architecture patterns, and engineering standards to accelerate enterprise AI adoption.
- Support technology assessments, AI strategy, solution roadmaps, effort estimation, and technical proposals.
- Ensure AI solutions align with enterprise architecture, data privacy, security, responsible AI, and governance standards.

Required Skills &

Experience :

- Robust experience in AI Solution Architecture, AI System Design, and enterprise AI engineering.
- Deep hands-on expertise in LLMs, Generative AI, RAG, embeddings, prompt engineering, and AI agents.
- Strong proficiency in Python and SQL.
- Experience designing and developing scalable full-stack AI applications.
- Strong knowledge of API architecture, REST APIs, microservices, and distributed systems.
- Strong experience with vector databases and enterprise search technologies.
- Extensive experience with at least one major cloud platform: Azure, AWS, or GCP.
- Strong understanding of CI/CD, DevOps, MLOps/LLMOps, Docker, Kubernetes, and production deployment.
- Experience designing enterprise-grade AI platforms with focus on security, scalability, reliability, observability, and cost optimization.
- Proven experience providing technical leadership and mentoring engineering teams.
- Strong stakeholder management, communication, problem-solving, and solutioning skills.

📌 Lead AI Engineer - Generative AI (India)
🏢 r3 Consultant
📍 India