We’re a fast-growing cloud-native digital bank looking for a Lead SRE / Platform Engineer to drive initiatives to improve the reliability, resilience, and efficiency of the platform that powers our products. This is a hands-on role with leadership responsibilities: you’ll help set the technical direction for how we operate, scale, and secure our infrastructure, and you’ll build the systems that let us move quickly without breaking things.
You’ll be part of a core team that is trusted to keep it running smoothly, make it faster, and turn toil into code.

What you’ll do

• Own the observability platform. Logs, metrics, traces, events — the full picture. Make signals actionable, reduce alert fatigue, and give every team the tools to understand their systems in production.
• Build agentic SRE systems. Design and ship AI agents that investigate incidents, execute runbooks, automate remediation, and close the loop on production issues. Bring context and harness engineering discipline to agent design: grounded context, well-scoped tools, evals, and guardrails.
• Automate the platform. Turn manual processes into self-service. Every recurring operational task is a candidate for elimination.
• Drive resiliency automation. Lead initiatives such as chaos engineering, cost and performance optimization, and capacity forecasting.
• Lead through influence. Set technical direction, mentor engineers, help unblock them when they hit issues, and write the design docs that get referenced for years. Raise the bar on what “operational excellence” means here.
• Be the calm in the storm. Jump in to diagnose and remediate critical infrastructure issues. Support the team during critical deployments — you're expected to be hands-on when it matters most.

What we’re looking for

• 8+ years in SRE, platform, or infrastructure engineering, with deep production ownership of a non-trivial cloud platform.
• Strong system design chops. You can reason end-to-end about distributed systems and event-driven architectures, including failure modes and the tradeoffs between consistency, availability, latency, and cost.
• AWS and Kubernetes experience. Comfortable with EKS internals, networking, IAM/IRSA, service mesh (Istio), and the control-plane/data-plane split.
• Hands-on with the rest of our stack — Kafka, Aurora PostgreSQL / RDS PostgreSQL, S3, ELB — or equivalent managed data and networking primitives.
• Proven track record owning resiliency in a high-velocity environment: incident command, postmortems that actually change things, SLO-driven engineering, progressive delivery.
• Experience with context engineering and harness engineering — building LLM and agent systems where the prompt, retrieved context, tool surface, and eval harness are first-class engineering artifacts.
• A clear vision for what zero-downtime, zero-touch, zero-trust looks like in practice, and a track record of moving real systems toward it.
• Fluent in IaC (Terraform, Helm, or similar) and CI/CD. You believe platforms are products and treat them as such.
• FinOps mindset. Comfortable analyzing cloud cost reports, identifying inefficiencies, and driving optimization initiatives that balance cost with performance and reliability.

Our ideal candidate has

• Financial services or other regulated-environment experience.
• Experience integrating AI/LLM systems into production operational workflows — not just experiments.
• Expertise in OpenTelemetry and the broader observability ecosystem.
• Experience with Sumologic and PagerDuty (or equivalent observability and incident management tooling).
• A continuous learning mindset — you stay current and bring new ideas to the table.
• Strong stakeholder communication and management skills — you can translate technical concepts for non-technical audiences and build alignment across teams.

As a Backend Engineering Lead you'd be able to work on and solve many interesting challenges which we are facing, learn new ways of working, and help build delightful high quality products for our customers.  

Key Responsibilities

Technical Vision and Strategy

• Defines and communicates the technical vision and strategy for the engineering team, aligning it with overall business objectives.
• Evaluates emerging technologies and industry trends — including AI/ML capabilities, LLM-based tooling, and agentic frameworks — to inform technical roadmaps and architectural decisions.

Team Leadership and Mentorship

• Leads and mentors a team of engineers, fostering a culture of collaboration, innovation, and continuous learning.
• Provides guidance and support to team members, empowering them to grow both technically and professionally.
• Builds AI literacy across the team — ensuring engineers can effectively leverage AI-assisted development tools (code generation, automated review, test generation) while maintaining critical thinking about AI outputs.
• Coaches engineers on prompt engineering, AI pair-programming workflows, and when to trust vs. verify AI-generated code.

Project Management and Execution

• Manages technical projects from inception to delivery, overseeing planning, execution, and quality assurance processes.
• Coordinates with cross-functional teams, including product management, design, and QA, to ensure successful project outcomes.
• Incorporates AI-augmented estimation and planning tools where appropriate, while maintaining accountability for delivery commitments.

Architecture and Design

• Designs and implements scalable, maintainable, and extensible software architectures, considering factors such as performance, security, and scalability.
• Leads architectural discussions and collaborates with senior engineers to drive consensus on design decisions.
• Architects systems with AI integration patterns in mind — designing clean boundaries for AI/ML components, defining contracts between deterministic and probabilistic systems, and planning for model versioning, fallback strategies, and graceful degradation.

Technical Excellence

• Sets high standards for code quality, performance, and maintainability, and ensures adherence to best practices and coding standards.
• Conducts code reviews and provides constructive feedback to ensure the delivery of high-quality software solutions.
• Establishes guardrails and review standards for AI-generated code — ensuring it meets the same quality bar as human-written code, with particular attention to security, edge cases, and maintainability.
• Champions responsible AI practices: output validation, bias awareness, hallucination mitigation, and appropriate human-in-the-loop controls for critical paths.
  
  

Operational Ownership

• Takes end-to-end ownership of the systems the team builds — from deployment through production reliability, performance, and incident response.
• Drives operational maturity: SLOs/SLIs, alerting hygiene, runbook coverage, on-call readiness, and post-incident reviews that produce lasting improvements, not just action items.
• Ensures the team builds with production in mind from day one — observability, graceful degradation, and failure modes are first-class design concerns, not afterthoughts.
• Instils a "you build it, you run it" culture where engineers feel genuine accountability for what they ship and how it behaves under real-world conditions.

Problem-Solving and Decision-Making

• Demonstrates advanced problem-solving skills, leveraging deep technical expertise and critical thinking to address complex technical challenges.
• Makes informed decisions under uncertainty, weighing technical trade-offs and considering long-term implications.
• Knows when AI is the right tool for a problem and when it isn't — resists the pressure to "add AI" where simpler, more deterministic solutions are more reliable and cost-effective.
• Leverages AI-assisted analysis (log analysis, incident correlation, root cause detection) to accelerate debugging and reduce mean time to resolution.

Stakeholder Management and Communication

• Builds strong relationships with stakeholders, including product managers, business leaders, and other engineering teams, to understand requirements and priorities.
• Communicates technical concepts and project status effectively to both technical and non-technical stakeholders, adapting communication style as needed.
• Translates AI capabilities and limitations into business terms — sets realistic expectations around what AI can deliver, timelines for AI-powered features, and the ongoing cost of maintaining AI systems (compute, data quality, model drift).
• Educates stakeholders on the difference between demo-grade AI and production-grade AI, managing enthusiasm without stifling innovation.

Risk Management and Mitigation

• Identifies and mitigates technical risks proactively, anticipating potential issues and implementing contingency plans to minimize impact on project timelines and deliverables.
• Maintains a focus on security, privacy, and compliance requirements, ensuring that systems and processes adhere to relevant standards and regulations.

Continuous Improvement and Innovation

• Drives a culture of continuous improvement and innovation within the engineering team, encouraging experimentation, knowledge sharing, and adoption of new technologies.
• Promotes automation, tooling, and process enhancements to improve efficiency, reliability, and scalability of development workflows.
• Runs structured experiments with AI tools and workflows — measuring real impact on velocity, quality, and developer experience rather than relying on anecdotal evidence.
• Builds internal knowledge sharing around AI use cases, effective patterns, and lessons learned — creating a feedback loop that accelerates the team's AI maturity.
• Actively contributes to agentic AI workflows for automating repetitive engineering tasks (test generation, migration scripts, documentation, incident triage) while maintaining human oversight on critical decisions.
  
  

Leadership and Influence

• Leads by example, demonstrating integrity, accountability, and a commitment to excellence in all aspects of work.
• Inspires and motivates team members to achieve their full potential, fostering a positive and inclusive team culture.
• Positions the team as AI-forward without being AI-reckless — models the discipline of evaluating AI tools critically, adopting what works, and discarding what doesn't.
• Shapes the engineering organization's AI adoption narrative, contributing to company-wide standards, governance frameworks, and best practices for AI in production systems.

Role Specific Technical Competencies:

• Strong technical background in (AWS, Spring Boot, Kotlin, K8s, Microservices, Kafka, Istio, event-driven architecture).
• Exposure to Cloud Technologies, SRE (Site Reliability Engineering), and DevOps concepts with a focus on automation.

Our Ideal Candidate: 

• 10+ years of overall experience with the majority of this experience as an Engineering Lead or Manager.
• Experience working in Fintech / Financial organisations is an added advantage.
• At least 5 years of demonstrable leadership experience, at an individual team level and working across demographically distributed teams and organizational boundaries.
• Good experience in creating actionable strategies, with clear outcomes, and continuous measurement of progress.
• Ability to produce and communicate effective and insightful thought leadership on technical topics.
• Strong technical background in (AWS, Spring Boot, Kotlin, K8s, Microservices, Kafka, Istio, event-driven architecture).
• Exposure to Cloud Technologies, SRE (Site Reliability Engineering), and DevOps concepts with a focus on automation.
• Ability to take ownership and deliver results in a challenging client-facing environment.
• A degree in a relevant area of study would be helpful but not mandatory

As a Technology Intern, you will be part of Trust’s Engineering function and contribute to the design, development, and delivery of technology solutions across the bank. Depending on business needs and your interests, you may be placed in different teams such as Backend Engineering, Platform Delivery, or AI/Data Platforms. Team allocation will be decided closer to the internship start date.

You will work alongside experienced engineers in agile teams, gain exposure to modern tools and technologies, and make a tangible impact while learning how a digital bank operates at scale.

What You Will Do

• Contribute to real product or platform initiatives used by Trust customers and internal teams
• Work in agile, cross‑functional teams to design, build, test, and improve technology solutions
• Learn and apply modern software engineering, platform, and data concepts
• Collaborate with engineers, product owners, designers, and other stakeholders
• Ask questions, experiment, and continuously learn in a supportive environment

Who We’re Looking For

We are looking for interns who are curious, motivated, and excited about building technology.

You should have:

• Pursuing a degree in Computer Science, Information Systems, Engineering, Mathematics, Data, or related fields
• A genuine interest in technology and building software or platforms
• Keen interest in learning AI-native ways of working
• A proactive, curious mindset with a strong desire to learn and grow
• Persistence and a positive attitude when faced with challenges
• Willingness to ask questions and seek feedback
• Ability to work collaboratively with diverse teams
• Interest in FinTech or technology‑driven products
• Fundamental understanding of programming concepts
• Experience with any programming language (e.g. Java, Python, Kotlin, JavaScript, etc.)
• Familiarity with software development, data, or platform concepts is a plus, but not required 

If this sounds interesting to you, don’t wait – apply now and get in touch with our recruiters to find out more.

We hope to be part of your exciting career journey!