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The Senior ML Engineer position sits in the Standard Chartered office in Chennai.
• Hand over production-grade machine learning and generative AI solutions end-to-end, from business requirement and solution design through develop, validation, deployment, monitoring and improvement.
• Engineer governed, observable and auditable agentic AI systems, covering tool calling, retrieval and memory, sandboxed execution, policy and approval controls, tracing, telemetry and automated evaluations.
• Accelerate reliable delivery through coding agents and AI-assisted SDLC practices while ensuring responsible AI, security, model governance and measurable business outcomes.
Operate as a senior individual contributor in charge of designing, creating, deploying and enhancing production-grade ML, GenAI and agentic AI solutions, while delivering technical guidance and mentoring to engineering colleagues.
• Translate business goals into scalable AI systems and hand over machine learning solutions throughout design, coding, training, testing, deployment and iteration. Apply NLP, computer vision, classification, clustering, deep learning, optimisation, supervised and unsupervised techniques, and select suitable algorithms, software, hardware and integration methods.
• Engineer the runtime and control layers around models, covering tool calling, retrieval and memory, sandboxed execution, policy and approvals, tracing and telemetry, automated evaluations, audit trails, post-based access control and regulatory controls. Monitor model performance, remediate delivery and AI risks, enhance solutions after delivery, and use coding agents and AI-assisted SDLC practices to increase quality and consistency.
AI/ML solution engineering
• Design, code, train, test, deploy and iterate enterprise-scale machine learning and generative AI solutions that support predictions, recommendations, search and business development strategies.
• Apply NLP, computer vision and machine learning techniques to structured and unstructured data, and choose suitable algorithms, software, hardware and integration methods.
End-to-end delivery and business outcomes
• Understand business criteria, translate goals into scalable AI systems and provide committed scope to agreed schedules and quality standards.
• Establish scalable, efficient and automated processes for data analysis, model development, validation, implementation and post-delivery improvement.
Agentic AI harness engineering and production readiness
• Establish governed, observable and evaluated agent workflows using tool calling, retrieval and memory, sandboxed execution, policy and approval controls, tracing, telemetry and automated evaluations.
• Carry out testing and validation to ensure systems operate reliably and satisfy quality, security, responsible AI and production-readiness standards.
Responsible AI, risk and governance
• Implement audit trails, position-based access control, approval workflows and regulatory controls, and secure AI/ML development and validation follow Responsible AI guidelines and standards.
• Recognise and escalate delivery and AI risks, define remediation roadmaps, and use guardrails to mitigate hallucination, unsafe actions, data leakage and compliance violations.
Model performance and continuous improvement
• Monitor model and system performance after delivery, use relevant metrics to spot degradation or control gaps, and implement model improvements.
• Use coding agents and AI-assisted SDLC practices, covering GitHub Copilot, Claude Code, specification-driven development and open specification workflows, to upgrade delivery quality and consistency.
Technical Guidance and Collaboration
• Offer technical guidance and share practical engineering know-how to upgrade team delivery and solution quality.
• Coach and mentor colleagues on machine learning, agentic AI, harness engineering and AI-assisted SDLC practices.
• Collaborate throughout disciplines, uphold robust risk and conduct standards, exercise sound technical judgment and contribute to an inclusive, accountable engineering culture.
Technical guidance, collaboration and mentoring
• Give technical direction, coaching and mentoring to colleagues developing scalable machine learning and agentic AI solutions.
• Work collaboratively over engineering, product, design, infrastructure and interfacing programme teams, and create effective stakeholder relationships.
Must-have Competencies
• Machine learning, NLP and computer vision solution engineering
• Enterprise ML and GenAI development, validation and deployment
• Agentic AI harness engineering, tool use, retrieval and evaluation
Other Abilities
• Responsible AI, security, model governance and regulatory controls
• Model performance monitoring, observability and continuous improvement
• Software engineering, integration and AI-assisted SDLC
• Stakeholder management, cross-functional delivery and technical mentoring
• Solving intricate business problems by turning structured and unstructured data into reliable, production-grade AI capabilities.
• Engineering governed, observable and auditable agentic systems that execute safely in a regulated environment.
• Learning through experimentation and using modern AI-assisted engineering practices to upgrade quality, speed and measurable outcomes.
• Main Experiences: 8-12 years of relevant hands-on exposure to machine learning and software engineering, among them development, validation, deployment and improvement of enterprise AI/ML solutions.
• Certifications / Qualifications (only where mandatory): Master's degree with specialisation in Technology.
• Languages: English
We're an international bank, nimble enough to act, big enough for impact. For more than 170 years, we've worked to make a positive difference for our customers, communities, and each other. We question the status quo, love a challenge and enjoy finding new openings to grow and do better than before. If you're looking for a career with purpose and you want to work for a bank making a difference, we want to hear from you. You can count on us to celebrate your unique talents and we can't wait to see the talents you can bring us.
Our purpose, to drive commerce and prosperity through our unique diversity, together with our brand promise, to be here for good are achieved by how we each live our valued behaviours. When you team up with us, you'll see how we value difference and advocate inclusion.
Together we:
In line with our Fair Pay Charter, we offer a competitive salary and benefits to support your mental, physical, financial and social wellbeing.
Eligibility typically includes the qualifications and experience outlined in the job description above, with around 0-5 Years years of relevant experience expected for this role.
The key responsibilities for this role are detailed in the Key Responsibilities section above, covering the core duties expected of a Senior ML Engineer at Standard Chartered.
This role requires around 0-5 Years years of relevant experience, as specified in the job listing. Please refer to the Qualifications & Experience section above for full details.
Skills relevant to this position are outlined in the Qualifications & Experience section above. In general, strong communication, domain knowledge, and the ability to meet role-specific targets are valued across similar BFSI positions.
You can apply directly using the Apply Now button on this page, which will take you to Standard Chartered's application process for this role.