Job Description :

This role is a strategic platform engineering lead responsible for designing and scaling enterprise Generative AI, Data Science, Metadata, Data Quality, and Event-Driven platform capabilities within MALTS. The position will drive self-service AI enablement, platform modernization, and reusable enterprise services that accelerate AI adoption across Consumer, Banking, and Wealth businesses.

Key Responsibilities :

- Provide technical leadership, architectural direction, and platform engineering expertise across enterprise Generative AI, Data Science, Data Engineering, Event Streaming, Metadata, and Data Quality initiatives.

- Design and develop reusable platform services that enable end-to-end self-service AI and data workflows.

- Define and implement enterprise standards, reference architectures, and engineering best practices for scalable AI and data platforms.

- Lead the design and delivery of agentic AI applications, intelligent workflows, and event-driven architectures.

- Partner with business, product, architecture, and engineering teams to prototype solutions and accelerate innovation.

- Drive platform modernization initiatives leveraging cloud-native architectures, Kubernetes, containers, and distributed computing.

- Ensure platform solutions meet enterprise requirements for security, governance, resiliency, and operational excellence.

- Establish CI/CD, Infrastructure-as-Code, and DevSecOps practices.

Required Qualifications :

- Bachelors or Masters degree in Computer Science, Engineering, Data Science, or related technical discipline.

- 10+ years of hands-on experience designing and building enterprise-scale AI, Data Science, Data Engineering, Metadata, Data Quality, and Analytics platforms.

- Proven experience architecting and implementing enterprise Generative AI platforms, including LLM integration, agent frameworks, and AI governance.

- Strong experience building self-service platforms supporting the complete AI/ML lifecycle.

- Deep understanding of modern AI and data platform architectures, including storage-compute separation and containerization.

- Hands-on expertise with Python and modern AI/ML ecosystems.

- Strong experience designing event-driven and streaming architectures (Kafka, Spark, Flink).

- Experience building and deploying scalable AI and data workloads on Kubernetes and cloud-native environments.

- Experience implementing CI/CD, automated testing, and DevSecOps practices.

Preferred Qualifications :

- Experience building enterprise-wide GenAI ecosystems including AI gateways, model management, and vector databases.

- Experience with Retrieval-Augmented Generation (RAG), agentic architectures, and AI workflow orchestration.

- Experience establishing enterprise AI governance, compliance, and responsible AI practices.