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Position Purpose
• Lead people and deliver end-to-end engineering accountability for governed agentic AI solutions, from discovery and architecture through delivery, production adoption, support and continuous improvement.
• Translate business, risk, compliance and regulatory needs into secure, scalable, reliable, observable and auditable AI/ML solution designs that hand over measurable business outcomes.
• Develop reusable agentic AI engineering capabilities, covering orchestration, tool calling, retrieval, memory, human-in-the-loop controls, sandboxed execution, automated evaluation and AI-assisted SDLC practices.
Lead a multidisciplinary engineering team and take end-to-end accountability for the strategy, architecture, delivery, governance, production adoption and continuous improvement of secure, governed and measurable agentic AI solutions.
• Set the engineering direction and translate business, risk, compliance and regulatory needs into scalable solution designs. Guide implementation of orchestration, tool calling, retrieval, memory, workflow automation, human-in-the-loop controls, sandboxed execution and enterprise integrations, while guaranteeing security, reliability, resilience, observability, auditability and production readiness.
• Own execution from discovery through establish, testing, release, adoption and support. Lead and shape engineers, establish standards for code quality, automated testing and evaluation, oversee delivery and technology risks, embed responsible AI and control expectations, engage senior stakeholders, monitor operational and business outcomes, and create reusable patterns and AI-assisted SDLC accelerators that enhance quality, consistency and delivery speed.
Engineering strategy and architecture
• Define and execute the engineering strategy, technical direction, solution design principles and roadmap for governed agentic AI capabilities over the AI/ML delivery portfolio.
• Design and guide implementation of orchestration, tool calling, retrieval, memory, workflow automation, enterprise integration, human-in-the-loop controls and sandboxed execution patterns.
End-to-end delivery and business outcomes
• Own delivery from intake and discovery through design, create, test, release, production adoption, support and continuous improvement, with clear accountability for scope, quality, timelines and value.
• Partner with business, product, risk, compliance and technology stakeholders to agree priorities, outcomes, dependencies, controls and adoption needs.
Engineering excellence and production readiness
• Establish standards for code quality, design reviews, documentation, peer review, automated testing, prompt and model evaluation, release controls, traceability and evidence capture.
• Secure production readiness spanning security, reliability, scalability, resilience, observability, telemetry, support, incident response, rollback and operational handover.
Risk, responsible AI and governance
• Own identification, assessment, mitigation and escalation of delivery, technology, security, data, model, operational and regulatory risks.
• Embed responsible AI, model risk, data protection, access control, approval, auditability and governance controls, and offer evidence-based updates to decision forums.
Continuous improvement and reusable capability
• Monitor adoption, performance, evaluation results, control effectiveness, incidents and business outcomes, using evidence to drive remediation and product evolution.
• Create reusable engineering patterns, platforms and accelerators, covering AI-assisted SDLC practices that upgrade productivity, consistency and speed without compromising control.
People and Talent
• Set direction and drive high performance by translating strategy into clear direction, aligning individual goals to team outcomes and offering regular, constructive feedback.
• Create talent and capability through coaching and career conversations that create critical capabilities, stretch potential and prepare individuals for future openings.
• Lead by example by post-modelling the Bank’s valued behaviours, upholding solid risk and conduct standards, exercising sound judgment, and creating an inclusive team culture that supports wellbeing, values diverse perspectives, and drives accountability.
• Lead, handle and shape engineering teams by setting direction, clarifying priorities, allocating work, coaching engineers and creating a high-accountability, collaborative delivery culture.
• Develop capability through hiring, onboarding, mentoring, career development, know-how sharing, cross-skilling, succession planning and clear ownership of critical solutions.
Must-have Capabilities
• Agentic AI solution architecture and engineering design
• Engineering leadership, team development and delivery accountability
• Enterprise software engineering, platform integration and AI-assisted SDLC
Other Competencies
• Responsible AI, model risk, data protection and control governance
• Production readiness, observability, resilience and operational support
• Senior stakeholder management, architecture governance and decision facilitation
• AI/ML delivery lifecycle, evaluation and continuous improvement
• Key Experiences: 8-12 years of relevant hands-on experience, such as engineering leadership and delivery of enterprise AI/ML or agentic AI solutions in regulated environments.
• Certifications / Qualifications (only where mandatory): Master's degree with specialisation in Technology.
• Language: English
• Solving involved business, risk and regulatory problems through secure, governed and measurable AI engineering.
• Creating high-carrying out engineering teams and reusable capabilities that strengthen delivery quality, speed and consistency.
• Taking solutions from ambiguity and design through reliable production adoption, then upgrading them using operational evidence and business outcomes.
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.
This Lead AI/ML Solutions vacancy is based in Chennai.
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 Lead AI/ML Solutions 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.