AI Enablement Engineer - Ref. 86ca3nqjg

At Medicilio, we're building the bridge from remote patient monitoring to remote patient intelligence — software that doesn't just track healthcare operations, but actively improves clinical decisions and patient outcomes at scale.

We are a remote-first company with teams across multiple continents, building products for Italy today and expanding across Europe tomorrow.

You'll work closely with product, operations, clinical stakeholders, and external technical partners to solve complex real-world problems in a high-trust environment where ownership matters more than process.

We care about:

• shipping meaningful products that work in production,
• building systems that remain maintainable over time,
• learning fast and adapting continuously,
• and enjoying the process of building together.

his is a cross-functional role that designs and builds how AI is used across Medicilio.

We are looking for a hybrid profile: someone who can sit with stakeholders from operations, clinical, and product, understand their workflows, and coordinate across teams — and go deep into the technical implementation, building AI systems and shipping working software hands-on. Either side alone is not enough.

You will report to the VP of Engineering, but your work spans the whole company. Wherever AI can create real leverage, you help identify it, design the right approach, and build the systems that make it work. You also pick up individual high-impact use cases when the situation calls for it.

This is not a role about deploying AI tools. It is about designing a coherent AI system — how it fits together, how teams use it, what guardrails apply, where it should not be used, and how it stays useful as both the technology and the company evolve.

We operate an artifact-driven delivery model. Your work shapes how AI fits into it. We expect teams to remain capable of operating without AI when required — AI increases leverage, it does not replace human judgment.

• Build a coherent picture of how AI should be used across Medicilio — engineering, product, operations, clinical workflows.
• Identify where AI creates real leverage and where it adds risk or noise.
• Define principles, patterns, and guardrails that apply consistently across teams.
• Ensure individual AI use cases reinforce each other rather than fragment into disconnected experiments.

Example: Mapped how AI was being adopted independently across three teams — engineering using AI for code, operations using AI for patient triage notes, and product using AI for spec generation. Identified shared infrastructure needs (auth, audit, prompt versioning, evaluation patterns) and designed a unified internal AI layer. Result: faster adoption across teams, consistent compliance posture, and significantly reduced duplication.

• Work directly with engineering, product, operations, and clinical stakeholders to understand their actual workflows and problems.
• Translate non-technical needs into technical AI solutions — and translate AI capabilities back into language stakeholders can act on.
• Earn trust across teams: your work depends on people actually adopting what you build, which depends on them believing you understand their context.
• Run discovery, gather feedback, and prioritise based on real impact rather than enthusiasm.

Example: A clinical operations team was spending hours each week reconciling notes across systems. Spent two weeks embedded with them, understood the workflow, identified the specific points where AI could compress the work without compromising clinical accuracy, designed and shipped an internal tool with appropriate human-in-the-loop checks. Saved the team meaningful hours per week and built credibility for future AI work in clinical contexts.

• Design and maintain the agentic engineering workflows that make AI-assisted development safer and more productive.
• Build internal tooling — CLAUDE.md systems, MCP integrations, sandboxed agents, evaluation harnesses, observability for AI work.
• Define the patterns engineers, product, and operations follow when working with AI.
• Continuously evaluate new tools, models, and approaches as the space evolves.

• Pick up specific AI-powered use cases across the company when they have outsized impact.
• Build them in a way that informs the broader system — every individual use case becomes a reference example others can learn from.
• Stay hands-on enough in the work to keep your system-level decisions grounded in real engineering and product reality.

Healthcare is a regulated environment. AI introduces new compliance surface area — data handling, model behavior, audit trails, human oversight. Getting this wrong has real consequences.

• Partner closely with the GRC team to make sure every AI solution meets compliance requirements (GDPR, EU AI Act, Italian healthcare law, internal data governance).
• Coordinate compliance reviews into the design phase, not after the fact — making the right thing the default, not an afterthought.
• Build the technical patterns that make compliance enforceable in code: audit logging, data isolation, prompt versioning, evaluation evidence, human-in-the-loop checks.
• Stay close to evolving regulation and translate it into practical engineering guidance the rest of the company can act on.

• Make AI use measurable: define what "good" looks like and how we know we're getting better at it.
• Document patterns, anti-patterns, and learnings so the company improves over time.
• Identify and eliminate entire classes of friction, inefficiency, or risk in how AI is used — not just individual issues.
• Help individual contributors and leaders level up their AI fluency through pairing, documentation, and worked examples.

We operate in small, high-trust squads with strong ownership and minimal process overhead.

AI Enablement is a cross-functional role. You'll work with stakeholders across the company, partnering closely with Engineering Leads, Product Leads, Operations, and the VP of Engineering.

A typical week includes:

• working with stakeholders across teams to understand problems and design solutions
• building and improving the internal AI infrastructure
• pairing with engineers, PMs, or operations on AI-assisted work
• shipping high-leverage individual use cases
• evaluating new tools, models, and patterns as the space evolves
• documenting patterns and raising the company's AI fluency over time

We optimize for:

• clarity over process theater
• ownership over handoffs
• fast iteration with high standards
• continuous improvement of how the company builds and operates
  

• Python (Django / FastAPI)
• TypeScript (React)
• PostgreSQL
• Google Cloud Platform
• AI-assisted engineering workflows (Claude, Cursor, MCP, sandboxed agents)
• LLM application infrastructure (evaluation, observability, prompt management)
  

• 3+ years of engineering or a "technical AI setup" experience.
• You can sit in a room with stakeholders, coordinate across teams, and run a project — and go deep technically on AI implementation, with hands-on building skills. Strong at both sides. Either-or candidates will not succeed in this role.
• Deep, hands-on experience working with LLMs and AI tools — agentic workflows, prompt design, evaluation, integration patterns. You have strong opinions formed from real production experience.
• Demonstrated ability to work cross-functionally with non-technical stakeholders — operations, product, clinical, or business teams.
• Project coordination experience — gathering requirements, scoping, sequencing work across multiple teams, holding the thread end-to-end.
• Strong communication skills, including the ability to translate between technical and non-technical contexts.
• Strong systems thinking — ability to see how individual decisions affect the broader system across teams.
• Stakeholder management: building trust, navigating differing priorities, earning adoption rather than mandating it.
• Comfort partnering with GRC, compliance, or legal stakeholders — translating regulatory requirements into technical patterns engineers can actually implement.
• Comfort operating in ambiguous, evolving environments where the right answer changes monthly.
• Security and data protection awareness, especially in regulated contexts.
• Curiosity, adaptability, and an ownership mindset.
  

This is a hybrid role. Pure engineers without stakeholder fluency, or pure coordinators without technical depth, will struggle. The most valuable skills are:

• the ability to switch between stakeholder conversations and hands-on technical implementation in the same day
• judgment about where AI creates real value vs theatre
• systems thinking across an entire company, not just within a single team
• stakeholder management and the ability to build trust across functions
• ability to design abstractions and patterns that people across different roles actually adopt
• product thinking applied to internal systems — every team is your user, with different needs and priorities
• clear communication, especially when bridging technical and non-technical contexts
• patience and pragmatism — building company-wide systems is slower than shipping features, but compounds significantly

We are looking for engineers who can hold both sides — depth and breadth — and care about making the entire company more effective.

€60,000–€80,000 depending on experience.

Top of band is available for engineers with proven track record in cross-functional AI work, internal platform building, or agentic workflow design.

Equity might be part of the package, with allocation and vesting discussed openly at offer stage.