Senior ML Engineer

Job Summary
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 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 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
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 thing and 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-off including 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 working options based around home and office locations, with flexible working patterns.
Proactive wellbeing support through 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 culture to 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.

Work arrangement
No