About the Opportunity
Job Type: Permanent
Application Deadline: 07 September 2026
Job Description
Title Principal Engineer II
Department CSP
Location Bengaluru
Reports To Head of Platform Design and Solutions
Level 6
We share a commitment to making things better for clients and each other. We continually explore new technology and different ways of working to put our clients first. So, bring your boldest ideas to our (name of team/business area) team and feel like you are making progress.
About your team
You will join the CSP platform and design function operating across multiple squads and domains. The team drives technical excellence and consistency for agentic AI and MLOps capabilities, ensuring solutions are secure, scalable, reliable, and aligned to enterprise standards. We work in close partnership with Product, Architecture, Data Engineering, Security, and Platform teams.
Role summary
- Act as technical authority across squads, driving consistent engineering practices, architecture alignment, and engineering standards for agentic AI and ML-enabled services.
- Own the technical delivery of CSP's agent interoperability capability, including shared APIs, Model Context Protocol (MCP) servers, reusable agents, and agent-to-agent interaction patterns.
- Own technical direction and solve complex cross-team challenges across CSP platforms.
- Partner with Engineering Managers, Architects, and Product to shape scalable solutions and influence roadmaps.
- Ensure alignment with enterprise standards, security controls, and regulatory requirements.
- Champion engineering excellence by mentoring senior engineers, promoting reusability and automation, and supporting responsible AI adoption.
Key Responsibilities
Technical outcomes
- Drive technical alignment and architecture consistency across squads.
- Deliver reusable APIs, MCP servers, and controls for secure agent-to-agent and tool interaction across CSP.
- Ensure cross-team technical risks are identified, tracked, and mitigated.
- Support resolution of major production issues and escalations for AI and ML services.
Technical contribution
- Define technical direction and enforce engineering standards for agentic AI and MLOps.
- Lead complex design decisions and cross-team integrations across data, model, and platform layers.
- Design, build, and operate production-grade APIs, MCP servers, agent services, and tool integrations used across CSP.
- Define reusable agent interaction and orchestration patterns, covering contracts, versioning, resilience, tracing, security, and auditability.
- Build and improve CI/CD pipelines for ML and agentic workflows, including versioning and rollback controls.
- Promote reusability, scalability, and modernisation across solutions.
- Contribute to enterprise standards and engineering communities, including Inner Source practices.
- Mentor senior engineers and build technical capability across teams.
- Define and enforce non-functional requirements across squads, including reliability, resilience, security, and observability.
- Ensure future readiness through technology evaluation and modernisation planning.
Stakeholder management
- Partner with Product, Architecture, and business stakeholders.
- Influence roadmap and investment decisions using technical evidence and risk-aware options.
AI adoption
- Promote adoption of AI-assisted engineering and agentic delivery patterns at scale.
- Define guardrails, evaluation, and release controls for secure, reliable, and responsible AI usage.
Cost discipline
- Drive cost-efficient architecture and infrastructure decisions across squads.
- Evaluate build versus buy trade-offs for cross-team solutions.
- Ensure licensing, model serving, and cloud costs are optimized across the domain.
Success indicators
- Consistent engineering practices and adopted, reliable shared APIs, MCP servers, and agent interaction patterns across CSP.
- Reduced technical debt and improved platform scalability.
- Effective resolution of cross-team technical challenges.
- Strong technical capability across engineering teams.
- Strong adherence to non-functional requirements across teams.
About you
Core skills
- Proven experience as a senior engineer operating across multiple squads or a domain.
- Strong MLOps engineering capability: CI/CD for ML, model lifecycle, and production operations.
- Strong hands-on Python and service engineering, including secure APIs, distributed services, authentication, authorisation, and observability.
- Experience building MCP servers and clients, or equivalent AI tool-integration protocols, and designing resilient agent-to-agent workflows.
- Hands-on experience with container and cloud platforms, including Docker, Kubernetes, and AWS.
- Strong architecture and non-functional thinking across reliability, security, scalability, and performance.
- Ability to influence technical direction and collaborate effectively with Product, Architecture, and Engineering leadership.
- Demonstrated mentoring of senior engineers and contribution to engineering standards.
Bonus skills
- Experience in regulated financial services environments.
- Experience with agentic AI evaluation frameworks, agent interoperability standards, and safety or guardrail design.
- Exposure to model registry, feature store, and data quality frameworks.
- Experience with policy-as-code, platform security automation, and FinOps practices.
- Contribution to Inner Source or enterprise engineering communities.
Feel rewarded
For starters, we’ll offer you a comprehensive benefits package. We’ll value your wellbeing and support your development. And we’ll be as flexible as we can about where and when you work – finding a balance that works for all of us. It’s all part of our commitment to making you feel motivated by the work you do and happy to be part of our team. For more about our work, our approach to dynamic working and how you could build your future here, visit careers.fidelityinternational.com.
For more about our work, our approach to dynamic working and how you could build your future here, visit careers.fidelityinternational.com.
