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 :

- Strong 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.