Senior Software Engineer, Product & Platform

We are hiring a Senior Software Engineer, Product & Platform to help Moodsonic turn ambitious product direction into reliable software quickly.

This is not a prompt-only role, an AI research role, or a conventional ticket-taking software role. It is a senior individual-contributor role for someone who can take clear product direction, working prototypes, rough specs, and AI-generated code, then turn them into secure, tested, production-ready software.

The right person uses AI agents as a serious engineering multiplier while staying personally responsible for correctness. You should be able to design the loops and structures that let a small team ship far more product than its headcount would normally allow: clear specs, repo rules, implementation loops, test loops, review loops, QA loops, worktree discipline, and practical production gates.

We do not need someone to arrive with our exact AI workflow on day one. We do need evidence that you have already taken these tools seriously, can learn quickly, and can apply them with engineering discipline rather than treating generated code as self-validating.

What you will work on

You will turn product specs, design prototypes, and rough AI-built implementations into production-ready software.

You will build and improve AI-assisted engineering workflows: repo instructions, reusable prompts, code-agent loops, implementation plans, review checklists, test generation, QA passes, and documentation flows.

You will own shippable product slices across the stack, including Python backend services, APIs, data models, TypeScript/React product surfaces, internal tools, and operational glue.

You will harden the platform as it grows: identity and authentication, roles and permissions, multi-tenant isolation, API contracts, data protection, observability, CI/CD, and release discipline.

You will build tests around the risks that matter: tenant boundaries, data durability, API compatibility, auth behavior, deployment safety, and customer-facing workflows.

You will review AI-generated and human-written code with the same standard: what can break, how we know, what we tested, and what still needs human judgment.

We are looking for deep production software and systems judgment, meaningful hands-on AI-assisted development exposure, strong communication, high agency, and the ability to turn rough product direction or prototypes into shippable software.

You should use AI coding agents or similar tools for more than snippets: planning, implementation, tests, review, refactoring, QA, documentation, and codebase understanding. You should be able to explain what you delegate, what you never delegate, what AI gets wrong, and how you verify the result.

We also care about the harder-to-train signals: clear software judgment, ownership, team fit, and willingness to figure out fast-changing tools before there is a stable playbook.

You should bring strong backend fundamentals: APIs, data modeling, SQL, migrations, reliability, observability, and production debugging. Strong Python for backend services is important, with enough TypeScript/React or Node experience to work across product surfaces when needed.

You should have practical security and enterprise readiness instincts: authentication, authorization, tenant boundaries, secrets, data protection, auditability, and deployment risk.

Required

• Deep production software and systems judgment.

• Meaningful hands-on AI-assisted development exposure.

• Experience turning rough product direction or prototypes into shippable software.

• Strong backend fundamentals: APIs, data modeling, SQL, migrations, reliability, observability, and production debugging.

• Strong Python for backend services, with enough TypeScript/React or Node experience to work across product surfaces when needed.

• Security and enterprise readiness in practice: authentication, authorization, tenant boundaries, secrets, data protection, auditability, and deployment risk.

• Testing and review discipline as part of the work, not as an afterthought.

• High agency in ambiguous work.

• Strong communication, EQ, and team instincts.

• Willingness to dive into frontier or ambiguous tools before there is a clear playbook, while staying grounded in production engineering fundamentals.

Valued

• Experience with FastAPI, Django, Postgres, React, Vite, Node, or similar production stacks.

• Experience with deployed devices, telemetry, provisioning, remote diagnostics, partner integrations, or customer environments with real operational constraints.

• Experience in regulated, security-conscious, healthcare, enterprise, or compliance-driven environments.

• Familiarity with CI/CD, infrastructure as code, monitoring, incident response, runbooks, backups, and recovery.

• Audio, signal processing, real-time systems, creative tooling, accessibility, or human-centered technology background.

• Experience introducing AI-assisted development workflows to other engineers without creating process drag.

Probably not a fit if

• You are only curious about AI-assisted development but have not yet used it for substantive software work.

• You trust generated code without serious review.

• You want a large team, perfectly-detailed tickets, and a mature process around you before you can be effective.

• You prefer platform cleanup over product delivery so strongly that customer-facing progress would slow down.

GBP 65,000-85,000 base salary, calibrated to experience. Participation in the company option scheme will be offered; equity will be a material part of the package.

In your note, please include 3-5 sentences on one production change where AI did a meaningful share of the engineering work. What did you delegate, what did you personally review, what did AI get wrong, and how did you know it was safe to ship?