Senior / Software Engineer

Own products end to end. You take a product from idea to design, build, test, ship, and operate. You use AI to move faster at every step, and you stay accountable for the result.

Turn repeated work into reusable AI assets. You build skills, tools, MCP servers, and agent workflows that your whole team uses every day, instead of one-off prompts that nobody can reuse.

Make AI output measurable. You build evals, tests, and guardrails so that we can trust what agents produce. "It looks right" is not enough.

Build loops, not tasks. You connect agents to CI, code review, observability, and incident data so the system gets better each time it runs.

Design clean, scalable services. You build the APIs and services behind our apps, including authentication and AI products.

Find where AI changes the product itself. You work with product, data, and design on ideas that go beyond changing how we build.

Explain trade-offs clearly. You analyse requirements, propose solutions, and say when AI is the wrong tool.

Raise the bar. You lead projects, mentor engineers, and share what works so the whole org becomes more AI-native.

Strong fundamentals. You know system design, data structures, testing, and debugging. AI makes good engineers better. It does not replace fundamentals.

Proof that you applied AI, not only used it. Show us something you built.

Ownership and a positive attitude toward change. You push things forward, work across product and design, and are excited by how fast AI is changing engineering.

Learning velocity. You pick up new tools fast, and you drop old habits when a better tool arrives.

Experienced engineers: you have built and run services in the cloud. Experience with consumer-facing products is a plus.

Fresh graduates: side projects, hackathons, internships, and open source all count.

You built agent infrastructure at scale: sandboxes, runtimes, orchestration, or permission models.

You designed LLM-as-judge or eval pipelines that the team trusts.

You shipped AI features to real users and handled cost, latency, and failure modes.

You put AI into the SDLC: automated MR review, test generation, or incident triage.

You have worked on security, identity, or authentication systems.

Core: TypeScript / Node.js, React, Postgres, Kafka, SQS, Kubernetes on AWS, Knative, Debezium

Delivery & Ops: GitLab CI, Datadog

Data: Redshift, Spark, S3

AI: Claude Code / Codex, MCP