We're seeking top talents to develop high-quality machine learning models, services, and scalable data processing pipelines.
As an Applied AI/ML Engineering Director within Consumer & Community Banking, you will be a hands-on technical leader who drives the adoption of AI-powered solutions to solve real business problems at speed and scale. You will collaborate across lines of business and functions to architect, develop, and productionize high-quality machine learning models, agentic AI systems, and intelligent platforms that deliver measurable impact on both technology and business outcomes.
A core part of your mission will be accelerating developer productivity by identifying and solving engineering bottlenecks, championing AI4Tech adoption, and empowering engineering teams to build smarter and faster using modern AI tooling and practices. You will design and implement highly scalable, reliable data processing pipelines and drive analysis and insights that optimize business results.
Job Responsibilities
- Lead and influence technical direction across multiple teams and geographies, setting a clear and compelling engineering vision for Applied AI/ML.
- Motivate and align engineers around a common technical strategy, fostering a culture of innovation, experimentation, and continuous improvement.
- Represent and defend engineering decisions for the Hyderabad location, serving as the senior technical voice in cross-site collaboration.
- Architect and deliver production-grade Agentic AI and AI/ML solutions using Java, Python, and modern AI frameworks to rapidly address business challenges.
- Design, build, and deploy end-to-end AI systems including Retrieval-Augmented Generation (RAG) architectures, intelligent agents, and scalable ML platforms.
- Experiment with emerging AI technologies and rapidly prototype solutions that translate into production-ready products.
- Champion and accelerate AI for Tech adoption across engineering teams, enabling developers to leverage AI-assisted tooling for code generation, testing, debugging, and workflow automation.
- Identify and resolve engineering friction points by introducing AI-driven solutions that measurably improve developer velocity, code quality, and operational efficiency.
- Establish best practices, reusable patterns, and reference architectures that make it easy for teams to integrate AI into their daily engineering workflows.
- Design and implement highly scalable and reliable data processing pipelines that power AI/ML models and business analytics.
- Perform deep analysis and generate actionable insights to promote and optimize business results.
Required qualifications, capabilities and skills
- Deep, hands-on proficiency in Java and Python with extensive experience designing, building, and deploying AI/ML solutions in production environments.
- Demonstrated expertise in Agentic AI, Retrieval-Augmented Generation (RAG), large language model integration, prompt engineering, and related modern AI architectures.
- Proven track record of leading and influencing cross-country engineering projects through collaboration and technical authority — not just management.
- Ability to rapidly translate complex business problems into pragmatic, scalable AI/ML solutions with measurable outcomes.
- Experience driving engineering productivity improvements through AI-powered tooling, automation, and modern development practices.
- Exceptional ability to explain complex technical concepts clearly, craft compelling technical narratives, and influence senior stakeholders and engineering teams alike.
- Relentless focus on raising the bar — improving product efficiency, system health, and team capabilities through sustained technical contributions.
- A portfolio of tangible results showing how your technical leadership improved product quality, engineering velocity, or business performance.
Preferred qualifications, capabilities and skills
- Experience leading developer productivity initiatives within a large-scale engineering organization.
- Familiarity with enterprise-scale ML platforms, MLOps practices, and CI/CD pipelines for AI/ML workloads.
- Exposure to financial services, consumer technology, or similarly regulated, high-scale environments.