Agile Lab is a company founded in 2014 with the mission to create value for its customers in data-intensive environments through customisable solutions that establish performance-driven processes, sustainable architectures and automated platforms based on data governance best practices. Having delivered over 100 successful Elite Data Engineering initiatives, we have used this experience to create Witboost: a modular, technology-agnostic platform that enables modern organisations to discover, value and leverage their data in both traditional environments and fully compliant Data Mesh architectures. With a highly skilled team of over 300 data engineers based in Europe, Agile Lab helps organisations with their data-driven transformation.

🚀 About the role

We are looking for a Senior AI Engineer who can design, build, and evolve production‑grade agentic systems using agent harness–based architectures.

This role is for engineers who understand that reliable AI agents are not built by prompt engineering alone, but by wrapping capable models in opinionated, lightweight infrastructure: harnesses that provide tools, skills, memory, evaluation, and governance—without over‑engineering orchestration logic.

You will work at the intersection of backend engineering, agent runtime architecture, semantic systems, and modern AI evaluation, building long‑running, self‑correcting agents that operate under real‑world constraints.

💰RAL: € 48.5 - 62K

🎯 Key responsibilities

Build and operate modern AI systems in production

You will help build an AI Operating System: a harness‑centric runtime that turns powerful foundation models into reliable, long‑running systems. This AI OS provides structured capabilities—tool and filesystem access, code‑as‑action execution, skills loaded on demand, semantic grounding via knowledge graphs and ontologies, sub‑agent delegation, memory, and built‑in evaluation loops. Rather than hard‑coding workflows, the OS enables declarative agent design, where behavior emerges from clearly defined capabilities, constraints, and feedback—allowing agents to operate safely under uncertainty, adapt as models evolve, and remain maintainable in production.

Semantic and knowledge-driven intelligence

• Model and leverage semantic layers across data and services:

  • Knowledge graphs and entity relationships

  • Ontologies and semantic metadata (taxonomy, schemas, reasoning cues)

  • Semantic models enabling better retrieval, grounding, and explainability

• Integrate semantic systems with retrieval and agentic workflows to support:

  • Grounded answers, better contextualization, traceability

  • Consistent domain alignment and reduced hallucinations

Backend engineering excellence

• Build production-grade backend services and APIs that expose AI capabilities

• Design for:

  • Scalability, latency, and cost management

  • Reliability, observability, and maintainability

  • Security and data governance constraints

• Contribute hands-on to implementation, architecture, and engineering standards:

  • Clean architecture, modular design, testing strategies

  • CI/CD maturity, structured logging, metrics and tracing

Technical judgment and cross-team collaboration

• Evaluate new AI technologies quickly, understanding:

  • when to adopt, when to wait, and how to mitigate risks

• Collaborate with product, platform, and data teams to translate requirements into:

  • architecture decisions

  • delivery plans

  • measurable outcomes and quality gates

• Produce high-quality technical documentation and architecture artifacts

Tackle uncertainty and complexity head-on

• Operate effectively in ambiguous problem spaces where:

  • requirements are incomplete or evolving

  • solutions are not yet clearly defined

  • trade-offs must be surfaced early and revisited often

• Break down complex problems into incremental, testable solutions

• Make technical decisions under uncertainty and iterate safely, using:

  • experimentation and fast feedback loops

  • observability and measurable outcomes

  • fallback and rollback strategies

• Help teams and stakeholders navigate complexity with clear technical thinking and pragmatic choices

✅ Requirements (at least 2 must-have)

Strong backend engineering foundation

• Senior‑level experience as a backend software engineer

• Proven ability to build production systems that are observable, evolvable, and maintainable

• Deep understanding of distributed systems trade‑offs

Modern AI & agentic systems

• Hands‑on experience building LLM‑based agentic systems

• Familiarity with:

  • agent runtimes

  • harness patterns

  • long‑running agent behavior

• Clear understanding of pros and cons of new AI capabilities, not hype‑driven adoption

Semantic systems

• Experience or strong familiarity with:

  • knowledge graphs

  • ontologies

  • semantic modeling approaches

• Ability to integrate structured knowledge into agent workflows

Strong productivity in Spring or Python (at least one at pro level)

• Pro‑level proficiency in at least one of:

  • Python (agent harnesses, code‑as‑action, evaluation pipelines)

  • Java + Spring / Spring Boot (enterprise‑grade backends and integrations)

• Comfortable switching between AI logic and core backend code

🧠 Expected mindset & soft skills

• Ownership & proactivity: you don’t wait for perfect conditions

• Strong technical judgment: you can say “no” to bad ideas and propose better ones

• Agillity: long term vision balanced by pragmatic ability to deliver effective increments.

• Clear communication: you can explain decisions and trade-offs to technical and non-technical stakeholders

• Bias toward action: you turn ambiguity into execution plans and working systems

🧪 Hiring process note

[Application => Screening => Tech Interview (1 or 2) => Behavioral Interview]

Shortlisted candidates will take part in a live agentic coding session.
The exercise focuses on designing and implementing a software from scratch using whatever tool and AI-assistant you want. We value mature productivity of modern AI-driven development approaches.

🙌🏻 We offer:

• Full Remote or hybrid working in our offices: Milan, Turin, Padua, Bologna, Catania and Rende;

• Real work life balance;

• Training monthly budget (time and money);

• A structured career path with clear expectations and salary for each level;

• Support of a buddy in the first week of work;

• A coach as a guide in choosing the most suitable experiences for your ambitions;

• Benefits and corporate welfare programs: company prizes and welcome pack with all the equipment you need to work;

• Agile Nomads Experience: opportunity to work for 2 weeks abroad;

• Referral bonus, if you bring people as talented as you;

• The opportunity to attend one conference per year;

• A company rated 4.8 out of 5 for employee satisfaction on Glassdoor and certified as a Great Place to Work
  

• Inclusive environment where you can be who you really are;

• Stimulating environment oriented to growth, both professional and personal.

😊 How we work:

• We don't like hierarchies: we work as a team;

• We don't like bureaucracies, we prefer sense of responsibility;

• We like data, certainly, so anything that is measurable;

• We want to make a positive change in our industry;

• Empathy, humility, collaboration, and willingness to challenge ourselves are the basis of our work.

Please note: only candidates based in European time zones (CEST or similar) will be considered for this position.

This position is open to all candidates, regardless of gender, and we strongly encourage applications from both male and female candidates pursuant to Art. 27 of Legislative Decree no. 198/2006, as well as those who identify themselves as non-binary or of any other kind. We are committed to fostering a diverse and inclusive workplace.