SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
Alan is a prevention-focused health insurance company serving 1M+ members across Europe and Canada. The Ops AI Platform team is building a novel system where AI agents autonomously handle operational work—connected to internal and external tools, trained by non-technical operators, and running safely at scale.
You'll join at ground zero of this newly launched initiative, shaping the architecture and product foundation. The role sits at the intersection of product engineering and ML engineering, with a focus on delivering an outstanding experience to Alan's internal Ops team.
Key responsibilities:
- Integrate AI agents into internal tools with context-aware chat interfaces, allowing operators to review agent reasoning, actions, and provide follow-up instructions with instant business impact
- Empower non-technical users to autonomously create and maintain agents for operational task automation at scale: prompting, tool configuration, testing, datasets, replayability, and evaluations
- Build a comprehensive toolset for AI agents, connecting to internal business data, external servers, and web dashboards without APIs
- Implement safety and accuracy mechanisms: robust testing, stateful evaluation, progressive rollout, human-in-the-loop validation for sensitive actions, fine-grained permissions and limits
- Implement new agent capabilities and tooling for ops autonomy
- Scale the system from dozens to tens of thousands of automated daily tasks
- Influence system architecture and code quality
- Mentor engineers and contribute to product and engineering culture, including hiring
You'll work on LLMs, tool calling, human-in-the-loop systems, internal tools UX, multi-agent architectures, and see your work deployed in production immediately.
Within 6–12 months, the scope expands to own the AI platform powering all agentic use cases at Alan, creating economies of scale through common infrastructure and tooling.
Requirements:
- 3+ years of product/fullstack software engineering experience
- Strong background in full-stack software engineering and systems architecture, with a spike in back-end engineering
- Experience with traditional ML, LLMs, or ML DevOps is a plus
- Product-minded: solutions must be used and impactful
- Self-directed and pragmatic
- Humble and willing to grow
- Collaborative, enthusiastic about teamwork, learning, and teaching
- Fluent in English (French not required)
- High potential and curiosity valued above all
- Target hire level: D–F range (mid-to-senior IC or early management)
