Job Description :

We are looking for a Senior AI Architect to drive the delivery of enterprise-grade AI and Generative AI solutions. This role focuses on translating high-level solution designs into scalable, production-ready systems and leading engineering teams through execution.

You will play a critical role in building and deploying LLM-powered applications, Agentic workflows, and AI platforms, ensuring performance, scalability, and governance across implementations.

Key Responsibilities :

1. Solution Architecture :

- Lead the technical design and implementation of AI/ML solutions.

- Break down solution concepts into actionable architecture and development plans.

- Define system components including APIs, data flows, and model integration.

2. Delivery Leadership :

- Act as the technical owner for AI project execution.

- Drive best practices across development, deployment, and monitoring.

- Ensure scalability, reliability, and maintainability of AI systems.

3. Hands-on Development :

- Build and optimize LLM-based applications, RAG pipelines, and AI services.

- Work with vector databases and knowledge retrieval systems.

- Troubleshoot complex integration and performance issues.

4. MLOps & Deployment :

- Establish and manage end-to-end ML/LLM lifecycle workflows.

- Implement CI/CD pipelines for model deployment and monitoring.

- Ensure smooth integration with cloud-based AI services.

5. Governance & Quality :

- Implement model monitoring, logging, and observability.

- Ensure compliance with enterprise AI standards and best practices.

- Support secure and responsible AI implementation.

6. Collaboration :

- Work closely with cross-functional teams including data scientists and engineers.

- Engage with stakeholders to ensure successful delivery outcomes.

- Support documentation, handovers, and knowledge sharing.

Must Have Skills :

- Strong experience in AI/ML Architecture and system design.

- Hands-on with LLMs, RAG, and GenAI applications.

- Proficiency in Python development.

- Experience with MLOps / model lifecycle management.

- Knowledge of cloud platforms (GCP or Azure).

- Experience with Docker and Kubernetes.

Requirements :

- 10+ years in software engineering/architecture.

- 5+ years of hands-on experience in AI/ML systems.

- Experience building and deploying AI solutions in production.

- Strong understanding of modern AI frameworks and tools.

Good to Have :

- Experience with Agentic AI / autonomous workflows.

- Exposure to AI governance, explainability, or monitoring tools.

- Familiarity with multi-agent frameworks.