- Design and architect enterprise-scale AI and Generative AI platforms.

- Lead the development and deployment of production-grade AI solutions using Large Language Models (LLMs) and Generative AI frameworks.

- Build and optimize Retrieval-Augmented Generation (RAG) pipelines and multi-agent AI systems.

- Develop advanced prompt engineering strategies to improve AI model performance and business outcomes.

- Establish AI evaluation frameworks for model quality, accuracy, safety, and performance.

- Design scalable MLOps pipelines covering model training, deployment, monitoring, and lifecycle management.

- Implement model serving, observability, monitoring, and governance best practices.

- Architect cloud-native AI solutions with a focus on scalability, reliability, and security.

- Collaborate with cross-functional teams, business stakeholders, and engineering leaders to deliver AI-driven business solutions.

- Ensure compliance with enterprise AI governance, security, and Responsible AI standards.

- Provide technical leadership, architectural guidance, and mentorship to AI engineering teams.

Required Skills :

- 8+ years of experience in AI/ML architecture and enterprise solution design.

- Strong expertise in :

1. Large Language Models (LLMs)

2. Generative AI

3. Retrieval-Augmented Generation (RAG)

4. Agentic AI and Multi-Agent Systems

5. Prompt Engineering

6. AI Evaluation and Model Validation

- Hands-on experience designing and deploying production-grade AI applications.

- Strong understanding of :

1. MLOps

2. Model Lifecycle Management

3. Model Serving

4. AI Observability

5. Enterprise AI Platform Architecture

- Experience with Databricks, MLflow, and Vector Databases.

- Knowledge of AI Governance, Responsible AI, and enterprise security practices.

- Experience building cloud-native AI solutions on platforms such as AWS, Azure, or Google Cloud.

- Excellent stakeholder management, communication, and leadership skills.