Software Engineering Team Lead

This is an Engineering Lead role in an AI-assisted engineering environment. This role reports directly to the Head of Engineering and operates as their close partner in running the domain — not waiting for direction, but bringing decisions, trade-offs, and proposals to align on. You will own a domain — its people, its code, its technical direction, and how it interfaces with product, design, and the rest of engineering. You'll lead multiple pods within the domain, define the technical bar with the Product Lead, and stay hands-on in the codebase. AI-assisted delivery shifts engineering leadership toward system design, architectural judgment, and quality ownership — not away from code, but deeper into the decisions that determine whether code stays maintainable as the system grows. We operate an artifact-driven delivery model:work is broken into structured, reviewable units
AI assists execution where appropriate
engineers design, review, refine, and approve each stage before it progresses
Engineering Leads define the boundaries within which AI and humans operate safely — and hold the bar by staying in the work, not stepping out of it. Teams must remain capable of shipping without AI. AI increases leverage — it does not replace engineering judgment.
What you'll do
Lead the domainOwn the people across your pods — performance reviews, 1:1s, growth, hiring.
Define and protect the technical bar across all pods.
Coordinate across pods — keep them aligned without slowing them with process.
Unblock engineers, escalate when needed, keep pods focused.
Partner with the Product Lead on roadmap and trade-offs.
Align with the Head of EngineeringAlign directly with the Head of Engineering on technical direction, domain priorities, and how your domain fits the broader engineering strategy.
Bring architectural and structural decisions for alignment where they carry cross-domain impact, set precedent, or hold significant risk — and own outright the decisions that sit clearly within your domain.
Surface risks, blockers, and trade-offs early and directly, rather than letting them compound or escalate silently.
Report on delivery, team health, and the state of your domain honestly — including what isn't working.
Own technical directionDefine epics, tickets, and technical planning across the pods.
Make architectural decisions that hold up across sub-areas — consistent patterns, clear interfaces, no silent divergence.
Set standards for how pods ship.
Resolve structural problems before they compound.
Example: Inherited a domain where pods had drifted into different patterns — three auth approaches, two test setups, inconsistent error handling. Aligned them on shared patterns over a six-week refactor while still shipping. Cross-pod review became meaningful again, onboarding dropped, release cycle time fell ~40%.
Stay hands-on in the code
Contribute to non-trivial work where architectural judgment is needed.
Review PRs across the domain — including AI-assisted — for system correctness and consistency.
Build the patterns and abstractions pods reuse.
Hold the bar by example, not by memo.
Example: An AI-generated PR implemented the feature correctly but coupled two services owned by different pods. Redesigned the boundary with the engineer and captured the pattern in md files so the agent would not propose the same shape again. One hour of work, weeks saved.
Shape the AI-assisted workflow
AI is part of the delivery system. Leads are responsible for how it behaves across the domain.Decide where AI fits and where it doesn't.
Define how work is decomposed for human vs AI execution.
Set guardrails, constraints, and review mechanisms.
Improve how pods work with AI over time.
Example: AI-generated PRs were correct but inconsistent across pods. Introduced shared CLAUDE.md execution rules and invariant-based review checklists. AI output shifted from "individually correct" to "systemically safe and consistent" — and senior review load dropped on every pod.
Own delivery and reconciliation
Keep artifact handoffs clean between phases.
Run post-release reviews. Feed learnings back into team, roadmap, and standards.
Eliminate classes of failure, not individual incidents.
Example: A production incident in a clinical workflow turned out to be a class of issue. Worked with QA and the Product Lead on invariant-based regression tests and stricter pre-release checks for clinical-facing changes. Similar issues stopped reaching production.
Hold up the culture
Medicilio's values — Trust, Care, Curiosity, Expansion, Joy — are real. The team will copy what you do, not what you say.Lead by example on directness, honesty, and follow-through.
Create space for engineers to grow, learn openly, and disagree without fear.
Protect autonomy, clarity, meaningful problems, and time to think.
Make it safe to fail, and visible to learn.
Notice when someone is struggling, drifting, or carrying too much — and act early.
Example: Noticed an engineer had become quieter in standups and was missing review feedback they normally caught. Followed up in a 1:1, learned they were overloaded across two projects, redistributed with the Product Lead. The engineer recovered without anyone needing to escalate — and checking quiet signals became part of how the pods operated.
How we work
We operate in small, high-trust pods with strong ownership and minimal process overhead.A typical week for an Engineering Lead includes:monthly 1:1s and growth conversations across the pods in your domain
shaping and scoping upcoming work with Product, across sub-areas
hands-on contribution and architectural decisions in the codebase
reviewing changes (human and AI-assisted) across pods with focus on system correctness and consistency
debugging production issues when needed
improving the pods' workflows and the systems they own
We optimize for:clarity over process theater
ownership over handoffs
fast iteration with high standards
continuous improvement of how we build software
Tech stackPython (Django / FastAPI)
TypeScript (React)
PostgreSQL
Google Cloud Platform
AI-assisted engineering workflows (Claude, Cursor, MCP, sandboxed agents)
What we're looking for
Must-haves5+ years of fullstack engineering experience, with at least 1 year leading engineers — ideally across multiple sub-teams or projects within a domain.
Strong backend experience in Python (Django or FastAPI).
Strong frontend experience in TypeScript (React or Vue).
Demonstrated ability to hold a technical bar across a team — through code, reviews, and standards, not just meetings.
Strong systems thinking and architectural judgment, including the ability to identify structural problems before they compound.
Experience using AI coding tools in real production workflows, with clear judgment on when they help and when they don't.
Experience leading PR review and code quality standards across a team or multiple sub-teams.
Strong product thinking — ability to connect technical decisions to user and business outcomes, and to partner credibly with Product.
Security awareness and secure API design fundamentals.
Strong communication and coordination skills — with engineers, with product, with non-technical stakeholders.
Comfort with ambiguous, evolving technical environments. Demonstrated adaptability across tools, domains, or responsibilities.
Nice-to-havesExperience in healthcare or regulated environments.
Familiarity with HL7, FHIR, or DICOM.
Experience with observability, CI/CD, or cloud infrastructure.
Experience designing or improving agentic engineering workflows beyond using AI assistants.
GDPR and data protection familiarity.
Italian language skills.
Skills that matter most in the AI era
As AI handles more implementation work, the most valuable engineering leadership skills become:judgment over output
systems thinking over isolated implementation
architectural decision-making at team scale
product understanding
quality and security evaluation
clear communication under ambiguity
the ability to grow other engineers in this environment
We are looking for Engineering Leads who can think deeply, take ownership of a domain, and consistently raise the standard of what gets shipped — across both people and code.
Compensation depending on experience.Top of band is available for Engineering Leads who demonstrate strong ownership, proven team leadership, and the ability to operate effectively in AI-assisted engineering environments. Equity might be part of the package, with allocation and vesting discussed openly at offer stage.

Work arrangement
No