We are seeking a highly skilled Enterprise AI Architect / Technical Lead to define, design, and implement AI solutions across the organization. The role requires expertise in AI/ML, Generative AI, and enterprise-scale AI architectures, along with strong hands-on experience in cloud platforms, model deployment, and MLOps practices. The candidate will collaborate with business, data, and engineering teams to drive AI adoption and transformation initiatives.

Key Responsibilities :

- Define and drive enterprise AI architecture and solution strategies.

- Design end-to-end AI/ML and Generative AI solutions, including data pipelines, model development, deployment, and monitoring.

- Identify business use cases and recommend optimal AI approaches.

- Architect AI solutions using cloud platforms such as Microsoft Azure, AWS, or Google Cloud.

- Implement LLM-based solutions, including Prompt Engineering, Retrieval-Augmented

Generation (RAG), and AI agents.

- Establish best practices for MLOps, model lifecycle management, and CI/CD pipelines.

- Ensure AI governance, security, compliance, and responsible AI usage across solutions.

- Collaborate with business, data, and engineering teams to deliver scalable and efficient solutions.

- Provide technical leadership, architecture reviews, and mentoring to development teams.
- Support enterprise AI adoption and transformation initiatives.

Required Technical Skills :

- Strong expertise in Machine Learning, Deep Learning, and Generative AI.

- Experience with LLM frameworks and AI platforms.

- Proficiency in Python, APIs, and microservices architecture.

- Hands-on experience with TensorFlow, PyTorch, or similar AI/ML frameworks.

- Knowledge of data engineering, embeddings, and vector databases.

- Experience with Docker, Kubernetes, and cloud-native deployments.

- Proven experience in enterprise-scale AI implementations.

- Strong stakeholder management and communication skills for cross-functional collaboration.

Preferred Qualifications :

- Experience in RAG, AI agents, and prompt engineering.

- Exposure to AI governance frameworks and responsible AI practices.

- Experience with end-to-end MLOps pipelines in production environments.

- Familiarity with high-scale cloud environments and multi-cloud deployments.