Senior ML Engineer

Standard Chartered; Chennai District; Hybrid; INR 4,000,000 - 7,000,000; Full time

Job summary

Standard Chartered is seeking a senior ML/GenAI engineer to design, build, deploy and improve production-grade AI solutions that scale across business units.

You will mentor engineers, implement end-to-end AI workflows and ensure governance, security and responsible AI across the product lifecycle.

Qualifications

• Eight plus years of hands-on ML/GenAI and software engineering experience.
• Strong background in enterprise AI design, deployment and governance.
• Experience with tool calling, memory, and audited AI workflows.

Responsibilities

• Design, build, deploy and continuously improve production-grade ML/GenAI solutions.
• Translate business goals into scalable AI systems across design, training and deployment.
• Mentor engineers and guide engineering best practices in AI SDLC.
• Ensure responsible AI, security, governance and compliant operations.

Skills

ML engineering

NLP

Computer vision

Education

Master's degree in Technology

Tools

GitHub Copilot

Claude Code

Job description

• Deliver production-grade machine learning and generative AI solutions end-to-end, from business requirement and solution design through build, validation, deployment, monitoring and improvement.
• Engineer governed, observable and auditable agentic AI systems, including 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.

Key Responsibilities

Operate as a senior individual contributor responsible for designing, building, deploying and improving production-grade ML, GenAI and agentic AI solutions, while providing technical guidance and mentoring to engineering colleagues.

• Translate business goals into scalable AI systems and deliver machine learning solutions across 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, including tool calling, retrieval and memory, sandboxed execution, policy and approvals, tracing and telemetry, automated evaluations, audit trails, role-based access control and regulatory controls. Monitor model performance, remediate delivery and AI risks, improve 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 growth 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 requirements, translate goals into scalable AI systems and deliver 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

• Build 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, role-based access control, approval workflows and regulatory controls, and ensure AI/ML development and validation follow Responsible AI guidelines and standards.
• Identify and elevate 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 identify degradation or control gaps, and implement model improvements.
• Use coding agents and AI-assisted SDLC practices, including GitHub Copilot, Claude Code, specification-driven development and open specification workflows, to improve delivery quality and consistency.

Technical Guidance and Collaboration

• Provide technical guidance and share practical engineering knowledge to improve team delivery and solution quality.
• Coach and mentor colleagues on machine learning, agentic AI, harness engineering and AI-assisted SDLC practices.
• Collaborate across disciplines, uphold strong risk and conduct standards, exercise sound technical judgment and contribute to an inclusive, accountable engineering culture.

Technical guidance, collaboration and mentoring

• Provide technical direction, coaching and mentoring to colleagues building scalable machine learning and agentic AI solutions.
• Work collaboratively across engineering, product, design, infrastructure and interfacing programme teams, and build effective stakeholder relationships.

Skills and Experience

Must-have Skills

• 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 Skills

• 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

Motivations

• Solving complex business problems by turning structured and unstructured data into reliable, production-grade AI capabilities.
• Engineering governed, observable and auditable agentic systems that perform safely in a regulated environment.
• Learning through experimentation and using modern AI-assisted engineering practices to improve quality, speed and measurable outcomes.

Qualifications

Key Experiences

• 8-12 years of relevant hands‑on experience in machine learning and software engineering, including development, validation, deployment and improvement of enterprise AI/ML solutions.

Certifications / Qualifications (only where mandatory)

• Master's degree with specialisation in Technology.

Languages

• English

About Standard Chartered

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 clients, communities, and each other. We question the status quo, love a challenge and enjoy finding new opportunities 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 work with us, you'll see how we value difference and advocate inclusion.

Together we:

• Do the right thingand are assertive, challenge one another, and live with integrity, while putting the client at the heart of what we do
• Never settle,continuously striving to improve and innovate, keeping things simple and learning from doing well, and not so well
• Are better together,we can be ourselves, be inclusive, see more good in others, and work collectively to build for the long term

What we offer

In line with our Fair Pay Charter,we offer a competitive salary and benefits to support your mental, physical, financial and social wellbeing.

• Core bank funding for retirement savings, medical and life insurance,with flexible and voluntary benefits available in some locations.
• Time-offincluding annual leave, parental/maternity (20 weeks), sabbatical (12 months maximum) and volunteering leave (3 days), along with minimum global standards for annual and public holiday, which is combined to 30 days minimum.
• Flexible workingoptions based around home and office locations, with flexible working patterns.
• Proactive wellbeing supportthrough Unmind, a market-leading digital wellbeing platform, development courses for resilience and other human skills,global Employee Assistance Programme, sick leave, mental health first-aiders and all sorts of self-help toolkits
• A continuous learning cultureto support your growth, with opportunities to reskill and upskill and access to physical, virtual and digital learning.
• Being part of an inclusive and values driven organisation,one that embraces and celebrates our unique diversity, across our teams, business functions and geographies - everyone feels respected and can realise their full potential.