Senior Ai Engineer
The Incubate Team explores and implements cutting‐edge applications in Agentic AI and Machine Learning. The team is currently building a platform for the safe execution of AI agents: an environment where autonomous agents can run, call tools, and act on data with isolation, policy enforcement, observability, and human oversight built in.
As a Senior AI Engineer / Backend Developer you will own the backend architecture of this platform — the services, APIs, and runtime that make agent execution reliable, secure, and scalable.
Experience: 5+ years in backend software development
Location / Work model: Rome (Italy) / Hybrid
Mandate Skills Education: Bachelor's or Master's degree in Computer Science, Software Engineering, or a related field
Backend development: 5+ years building and operating production backend services — REST/g RPC APIs, asynchronous processing, message queues, relational and No SQL data stores
Programming: Python (expert level); solid grounding in software design patterns, concurrency, and performance
Agentic AI / Gen AI integration: hands‐on experience building LLM‐powered services and agents — tool/function calling, orchestration (Lang Chain, Lang Graph), RAG — and integrating commercial and open‐weight LLM APIs
Security engineering: authentication/authorization, secrets management, sandboxing/isolation, audit logging, secure API design
Scripting &; OS: Bash shell scripting on Linux/Unix What you will do Design and build the backend of the agent execution platform: agent runtime and lifecycle services, tool/function‐calling gateway, sandboxed execution orchestration, policy and guardrail enforcement, and audit/observability pipelines.
Develop robust, well‐documented APIs (REST/g RPC)
and event‐driven components that let agents interact safely with enterprise systems, data sources, and MCP‐style tool servers.
Engineer for production from day one: concurrency, queuing, retries with backoff, idempotency, rate limiting, multi‐tenancy, and LLM cost/usage controls.
Integrate and operate LLMs and agent frameworks within the platform; build the abstractions that let the team swap models and providers without rework.
Implement security controls across the stack — least privilege, isolation boundaries, input/output validation, secrets handling, prompt‐injection and data‐exfiltration defences — and wire safety/regression test harnesses into CI/CD.
Own deployment and operations on Kubernetes: containerization, Helm/Ia C, monitoring, alerting, performance tuning, and incident response.
Provide technical leadership: lead architecture and code reviews, define backend and security standards, mentor engineers, and raise the engineering bar across the team.
Partner with the AI Engineer / Data Scientist to productionize prototypes, expose evaluation infrastructure, and turn research results into shipped components.
Collaborate with product, security, and platform teams; document designs through ADRs and communicate trade‐offs clearly.
What we are looking for:
BE/BTech or above in Computer Science, Software Engineering, or related stream; 5+ years of backend product development,
including 1–2+ years building LLM/Gen AI‐powered services
Expert‐level Python and strong software engineering fundamentals: clean architecture, automated testing, code review, version control, CI/CD
Proven experience designing, shipping, and operating distributed backend systems at scale (APIs, async workers, message brokers such as Kafka/Rabbit MQ, SQL/No SQL databases, caching)
Hands‐on experience integrating LLM APIs and agent frameworks (Lang Chain, Lang Graph, Llama Index) and vector/retrieval stacks into production services
Deep knowledge of Kubernetes and container‐based deployment; Docker proficiency
Strong security mindset: sandboxing, isolation, Auth N/Auth Z (OAuth2/OIDC), secrets management, auditability, and awareness of the OWASP Top 10 for LLM Applications
Bash shell scripting in Linux/Unix environments
Ability to lead ambiguous, greenfield technical work from concept to production with limited supervision
Excellent written and verbal communication skills; able to explain complex technical trade‐offs to mixed audiences Nice to have Java, Go, or Rust for high‐performance or integration components
Experience with container sandboxing and isolation technologies (g Visor, Firecracker, Kata Containers, Web Assembly runtimes)
Familiarity with agent interoperability protocols (MCP, A2 A) and tool/plugin ecosystems
LLMOps tooling: model serving (v LLM, TGI), tracing/observability (Open Telemetry, Lang Smith), evaluation frameworks
Cloud platform experience (AWS, Azure, GCP) and infrastructure‐as‐code (Terraform, Helm)
Knowledge of AI governance and responsible‐AI frameworks (e.g., NIST AI RMF, EU AI Act requirements)
Working knowledge of ML fundamentals (Py Torch/Tensor Flow) to collaborate e
As a Senior AI Engineer / Backend Developer you will own the backend architecture of this platform — the services, APIs, and runtime that make agent execution reliable, secure, and scalable.
Experience: 5+ years in backend software development
Location / Work model: Rome (Italy) / Hybrid
Mandate Skills Education: Bachelor's or Master's degree in Computer Science, Software Engineering, or a related field
Backend development: 5+ years building and operating production backend services — REST/g RPC APIs, asynchronous processing, message queues, relational and No SQL data stores
Programming: Python (expert level); solid grounding in software design patterns, concurrency, and performance
Agentic AI / Gen AI integration: hands‐on experience building LLM‐powered services and agents — tool/function calling, orchestration (Lang Chain, Lang Graph), RAG — and integrating commercial and open‐weight LLM APIs
Security engineering: authentication/authorization, secrets management, sandboxing/isolation, audit logging, secure API design
Scripting &; OS: Bash shell scripting on Linux/Unix What you will do Design and build the backend of the agent execution platform: agent runtime and lifecycle services, tool/function‐calling gateway, sandboxed execution orchestration, policy and guardrail enforcement, and audit/observability pipelines.
Develop robust, well‐documented APIs (REST/g RPC)
and event‐driven components that let agents interact safely with enterprise systems, data sources, and MCP‐style tool servers.
Engineer for production from day one: concurrency, queuing, retries with backoff, idempotency, rate limiting, multi‐tenancy, and LLM cost/usage controls.
Integrate and operate LLMs and agent frameworks within the platform; build the abstractions that let the team swap models and providers without rework.
Implement security controls across the stack — least privilege, isolation boundaries, input/output validation, secrets handling, prompt‐injection and data‐exfiltration defences — and wire safety/regression test harnesses into CI/CD.
Own deployment and operations on Kubernetes: containerization, Helm/Ia C, monitoring, alerting, performance tuning, and incident response.
Provide technical leadership: lead architecture and code reviews, define backend and security standards, mentor engineers, and raise the engineering bar across the team.
Partner with the AI Engineer / Data Scientist to productionize prototypes, expose evaluation infrastructure, and turn research results into shipped components.
Collaborate with product, security, and platform teams; document designs through ADRs and communicate trade‐offs clearly.
What we are looking for:
BE/BTech or above in Computer Science, Software Engineering, or related stream; 5+ years of backend product development,
including 1–2+ years building LLM/Gen AI‐powered services
Expert‐level Python and strong software engineering fundamentals: clean architecture, automated testing, code review, version control, CI/CD
Proven experience designing, shipping, and operating distributed backend systems at scale (APIs, async workers, message brokers such as Kafka/Rabbit MQ, SQL/No SQL databases, caching)
Hands‐on experience integrating LLM APIs and agent frameworks (Lang Chain, Lang Graph, Llama Index) and vector/retrieval stacks into production services
Deep knowledge of Kubernetes and container‐based deployment; Docker proficiency
Strong security mindset: sandboxing, isolation, Auth N/Auth Z (OAuth2/OIDC), secrets management, auditability, and awareness of the OWASP Top 10 for LLM Applications
Bash shell scripting in Linux/Unix environments
Ability to lead ambiguous, greenfield technical work from concept to production with limited supervision
Excellent written and verbal communication skills; able to explain complex technical trade‐offs to mixed audiences Nice to have Java, Go, or Rust for high‐performance or integration components
Experience with container sandboxing and isolation technologies (g Visor, Firecracker, Kata Containers, Web Assembly runtimes)
Familiarity with agent interoperability protocols (MCP, A2 A) and tool/plugin ecosystems
LLMOps tooling: model serving (v LLM, TGI), tracing/observability (Open Telemetry, Lang Smith), evaluation frameworks
Cloud platform experience (AWS, Azure, GCP) and infrastructure‐as‐code (Terraform, Helm)
Knowledge of AI governance and responsible‐AI frameworks (e.g., NIST AI RMF, EU AI Act requirements)
Working knowledge of ML fundamentals (Py Torch/Tensor Flow) to collaborate e