Principal Engineer - AI-First Engineering Pod Lead

About the job:

We are hiring a hands-on Principal Engineer to lead our new AI-first engineering pod — a small, high-trust team that ships real product and platform work in our existing payments platform and .NET codebase, then helps the rest of engineering adopt what works.

This is not an advisory architecture role. You will code, review, unblock, and ship — while building the AI-first operating model from evidence, not slides. Framework work should emerge from what the pod proves in production: tooling choices, review practices, guardrails, and team habits that other squads can pick up without reinventing the wheel.

Over time, as the model proves itself, the pod is expected to grow into a full scrum team and potentially split into two scrum teams, as delivery capacity and adoption mature. You should be comfortable starting lean and hands-on, then evolving into a team lead who can run backlog, ceremonies, and engineering practices at squad scale.

This role requires an experienced technical leader who can operate with minimal direction. We are looking for someone who can independently identify technical challenges, propose solutions, drive engineering outcomes, and influence both technical and business stakeholders. This is not a role for someone who requires significant hand-holding or constant direction.

AI-first means agent-first development with human checkpoints where they matter— orchestrated pipelines, not a developer alone in a chat window. Work flows through defined steps: intake → context assembly → agent execution → automated review gates → human approval → production.

Using Cursor or Copilot well is a baseline. We want someone who has designed and built agentic orchestration: pipelines that connect real systems (Slack, issue trackers, Git, CI/CD) and run multi-step agent workflows with guardrails, audit trails, and human escalation at the right points. It does not mean unmanned codegen or bypassing regulated change control.

Requirements

Key Responsibilities:

• Lead pod delivery end-to-end: backlog refinement, technical breakdown, implementation, review, and release.
• Rapidly prototype product concepts, customer opportunities, and internal tools, working closely with Product, the CEO, and Sales to validate ideas through lightweight MVPs and production-quality proof-of-concepts before committing to full-scale roadmap investment.
• Design and build agentic orchestration pipelines — e.g. a bug reported in Slack, Jira, or Linear flowing through triage, context gathering, fix attempt, PR creation, and automated review before a human merges.
• Wire agentic review triggers into PR and CI/CD workflows: security analysis, bug-risk review, test gap detection, dependency checks — with clear pass/fail/escalate behaviour and auditability.
• Use AI-assisted development responsibly across coding, testing, debugging, refactoring, documentation, and code review — coaching the pod without bypassing engineering fundamentals.
• Ship production-visible outcomes early and codify what works into standards, guardrails, and tooling choices the wider org can adopt.
• Provide pragmatic technical leadership on pod-owned work and high-risk cross-cutting decisions; advise (not own) wider platform architecture.
• Lead one high-leverage modernisation path the pod can execute — including assessment of our .NET Core 2.1 estate and a pragmatic upgrade recommendation.
• Improve the pod’s path to production: Git workflows, CI/CD (Jenkins and/or GitHub Actions), testing expectations, and quality gates — then propose rollouts others can follow.
• Grow the pod: hiring, onboarding, scrum maturity, and mentorship across distributed and offshore engineers.
• Ensure everything ships with fintech-grade security, compliance, auditability, and operational discipline — including careful evaluation of AI vendor and tooling risk.
• Drive engineering initiatives independently, proactively identifying opportunities, removing technical blockers, and delivering business outcomes with minimal leadership oversight.
• Lead technical discovery and solution design for AI-enabled capabilities within an enterprise payments platform.
• Champion AI adoption across the engineering organisation by demonstrating practical, production-ready solutions rather than proof-of-concepts.

Tech stack:

• You do not need expert depth in every technology on day one, but you must learn quickly and reason credibly across the stack.
• Good to have - Frontend: React, TypeScript
• Backend: Strong hands-on .NET / C# experience (including legacy .NET Core 2.1 and modern .NET),
• Data: SQL Server
• Platform: AWS, Jenkins / GitHub Actions, Jira or Linear
• AI and agents: coding assistants plus agent orchestration — multi-step workflows, MCP/tool integration, PR and CI/CD hooks, webhook-driven pipelines (not GUI-only usage)

Qualifications:

• 12+ years in software engineering, with senior technical leadership experience.
• Strong hands-on skills in existing production codebases — not only greenfield.
• Experience leading or contributing materially to a small team shipping real work.
• Track record of mentoring and rolling out new engineering practices beyond your own team.
• Practical, safe use of AI-assisted development tools.
• Built agentic pipelines, not only used an AI IDE. You can describe systems you designed: triggers, agent steps, human checkpoints, CI/CD integration, and what happened when they failed.
• Strong hands-on experience with .NET/C# in enterprise production environments.
• React/TypeScript and .NET/C# experience; strong architecture and communication skills.
• Experience designing, developing, or implementing AI-enabled applications, agentic workflows, LLM integrations, or AI-assisted software engineering practices.
• Strong experience working within payments platforms, payment processing systems, fintech products, banking integrations, or other regulated financial environments.
• Legacy .NET Core upgrades
• Growing a pod or small team into a stable scrum team (or leading multiple squads).
• Creating engineering standards, test automation, or secure SDLC practices.
• Using AI to accelerate codebase comprehension, refactoring, test generation, or migration planning.
• Agentic SDLC automation: PR review agents, security scanning in CI/CD, bug-triage-to-fix pipelines, Slack, Jira, or Linear integrations.
• Demonstrated ability to independently lead technical initiatives, influence engineering direction, and drive business outcomes without requiring detailed guidance from leadership.

How you work:

• You are pragmatic, hands-on, AI-forward but not reckless, and comfortable growing from pod lead into multi-squad leadership without losing technical credibility.
• You live in your AI tools — continually experimenting and learning as models, products, and practices evolve, and bringing that curiosity back to the team. You enjoy sharing what you know in open technical discussion: pairing, working sessions, and honest conversation about what works and what does not.
• You are proactive about business context — asking product and leadership the questions that sharpen technical decisions.
• You draw people in: you encourage engineers to speak up, challenge ideas, and contribute, and you create space for quieter voices as well as confident ones.
• You are comfortable operating in ambiguous environments, making sound technical decisions, prioritising work, and driving engineering outcomes with minimal direction. You naturally take ownership and lead by execution rather than waiting for instruction.

First 90 days:

• By day 90 we would expect you to have:
• A clear understanding of the platform, codebase, and key technical risks in the pod’s domain.
• Shipped or be imminently releasing at least one production-visible outcome using the AI-first workflow end-to-end.
• Partnered with Product, the CEO, and Commercial teams to deliver at least one rapid prototype or MVP that informed a product, customer, or strategic investment decision.
• Published playbook v1 (tooling, guardrails, review, testing) derived from real pod work — not theory.
• At least one agentic pipeline prototype live — e.g. automated PR/CI review trigger or intake-to-PR workflow — with documented human checkpoints.
• Coached at least two engineers outside the pod on practical AI-assisted workflows (pairing or secondment).
• Assessed .NET Core 2.1 and proposed a pragmatic migration path for the pod or a follow-on squad.
• Delivered an executive-readable summary: what worked, what didn’t, and a recommended rollout plan for the next squad(s).
• Independently identified and delivered at least one high-impact engineering improvement that materially improved delivery, platform capability, or AI adoption without requiring detailed direction from leadership.

Benefits

• Flexibility in work hours and location, with a focus on managing energy rather than time.
• Access to online learning platforms and a budget for professional development
• A collaborative, no-silos environment, encouraging learning and growth across teams
• A dynamic social culture with team lunches, social events, and opportunities for creative input
• Health insurance
• Leave Benefits
• Provident Fund
• Gratuity