Technical Delivery Manager

Aptagrim designs, engineers and deploys specialised enterprise AI products and intelligent workflow systems. Our systems are built around how a business actually works: its knowledge, its decisions and its day-to-day operations.
We are a growing team at an early stage. You will shape how we deliver, not just follow an existing playbook, and the AI systems you ship will run inside real enterprise operations.
The role
We're hiring a hands-on Technical Delivery Manager with deep Python and Django experience. You will own the delivery of enterprise applications and AI solutions, from discovery to production rollout.
This is a technical leadership role, not a task-tracking one. Expect to spend about 25–30% of your time hands-on: reviewing code, designing backend systems, unblocking engineers and building critical pieces or proofs of concept yourself. The rest goes to delivery ownership, client relationships, planning and team leadership.
What you'll do
Delivery ownership
Own delivery end to end: lead discovery, scoping, estimation, planning, build, testing, client acceptance, deployment and handover to support.
Plan realistically: set milestones, dependencies and acceptance criteria that account for technical uncertainty, data readiness and client inputs. Spot risks early and act on them.
Control scope and economics: assess the effort, schedule and commercial impact of change requests. Track utilisation, budgets and project margins, and correct course when they drift.
Run multiple engagements: balance engineering, QA, DevOps and business analysis capacity across projects, working with the Project Coordinator on tracking and reporting.
Technical leadership
Lead backend engineering: guide the design of scalable, secure and maintainable systems in Python, Django and Django REST Framework, including APIs, data models, background processing and enterprise integrations.
Stay hands-on: review code, debug hard production and performance issues, and build critical components or proofs of concept when the team needs it.
Integrate AI into products: work with the Head of AI's team to bring LLM, retrieval and agent capabilities into production applications, with sound data flows, failure handling and monitoring.
Set the quality bar: establish practical standards for testing, code review, documentation, security, observability and release readiness, and own production readiness: deployment, rollback, monitoring and incident response.
Clients and team
Own client communication: run delivery reviews, explain technical trade-offs in plain language, set expectations (including around AI accuracy and iteration) and resolve escalations.
Support pre-sales: turn business requirements into solution options, estimates, assumptions and statements of work.
Grow the team: mentor engineers and tech leads, help hire, delegate well, and use retrospectives to make estimates and delivery steadily more predictable.
What you'll bring
7+ years in software engineering, including at least 3 years in technical leadership with ownership of client-facing delivery.
Deep, recent, hands-on Python and Django: Django REST Framework, object-oriented design, application architecture and production troubleshooting. You still write and review code regularly.
Strong backend fundamentals: REST API design, authentication and role-based access, PostgreSQL (schema design, ORM, indexing, query tuning, migrations), and async processing with task queues and caching (for example, Celery and Redis).
Production engineering: testing, Git workflows, CI/CD, Docker, Linux and deployment on a major cloud, plus security, secrets management, logging, monitoring and incident investigation.
A track record of delivering enterprise applications from requirements through deployment and client acceptance.
Delivery and commercial skills: estimation, planning, risk and scope management, effort forecasting, and managing budgets and margins.
Clear communication: you can explain technical problems to non-technical stakeholders and handle difficult client conversations calmly.
Working knowledge of Agile delivery, applied pragmatically rather than by the book.
Nice to have
Experience shipping LLM-based features to production, such as RAG pipelines, agent workflows, prompt and output evaluation, or LLM API integration
Experience delivering projects where data quality, model accuracy or non-deterministic outputs shaped the plan
Hands-on experience with AWS or Azure services beyond basic deployment

Work arrangement
No