SENIOR FULL-STACK SOFTWARE ENGINEER
Pubblicato il 20-08-2026 - Philogen in Italia
Department: Others
About Philogen
Philogen S.p.A. is a biotechnology company dedicated to developing targeted products for the imaging and treatment of life-threatening diseases, with a strategic focus on oncology. Our mission is to translate cutting-edge scientific innovation into effective therapies that improve patients’ lives.
Position Overview
At Philogen, we are building a small, high-impact engineering team focused on transforming core business processes into secure, production-grade software and AI systems. These systems support clinical operations, R&D;, finance, and corporate functions in a highly regulated environment.
We are looking for a Senior Full-Stack Software Engineer to take substantial ownership of the platform, its infrastructure, and its production deployments. This is a full-stack role in the literal sense: you will work across backend services, cloud infrastructure, deployment automation, frontend applications, identity, authorization, data systems, and isolated AI-agent execution environments.
Backend and infrastructure expertise are baseline requirements.
Strong frontend expertise is an additional advantage.
What You Will Do
— Build and operate Python services, APIs, CLIs, and full-stack workflows using TypeScript, React, and Next.js.
- Provision and run GCP and Kubernetes infrastructure through Pulumi, including databases, object storage, networking, secrets, deployments, and recovery procedures.
- Build secure execution environments for AI agents, covering sandbox lifecycle, tool access, workload identity, network isolation, resource limits, and persistent sessions.
- Integrate model providers and agent SDKs, including streaming, tool calls, cancellation, and long-running execution.
- Implement authentication, RBAC, least-privilege access, auditability, and compliance requirements as enforceable technical controls.
- Own production reliability across application and infrastructure layers through testing, observability, performance analysis