Scope
Per il seguente ruolo potrebbero essere richieste diverse soft skill ed esperienze. La preghiamo di consultare attentamente la panoramica riportata di seguito.
: Tech Lead / Forward Deployed Engineer for LLM + agentic architecture, infrastructure, and deployment
Salary range
: €70,000 – €90,000 gross/year (depending on experience)
About AI4I and our culture
The Italian Institute of Artificial Intelligence (AI4I) is headquartered in the standout location of
OGR, Turin
, with the unique mission to support both
applied research
and
on-site deployment of AI
. We are established to generate real-world impact at the intersection of science, technology, and industrial transformation.
Research units
focus on topics such as secure and trustworthy AI, physical AI and multi-agent systems while the
deployment unit
is responsible for the implementation at client site. A dedicated
high-performance computing center
, including next-generation GPU systems such as NVIDIA B200 accelerators, is available to support both research and industrial deployment.
Our
System User Knowledge platform
and OGR-based network connect corporates, SMEs, startups, and scientific partners, fostering workshops and collaboration with
external partners
to remain at the forefront of innovation.
Role Summary
Within the Deployment Unit, we are hiring a
Senior GenAI Engineer
who will operate as a
hands‑on tech lead for client projects
, owning end‑to‑end architecture, infrastructure, and production deployment of LLM applications. This is a highly hands‑on role for someone who combines strong
software engineering
skills with deep knowledge of the
GenAI ecosystem
, including RAG architectures,
orchestration frameworks
, evaluation methods, and
deployment patterns
.
You will work closely with:
Deployment and Data Science team
, delivering production‑grade AI services
Research units
, to industrialize promising use cases emerging from applied research
Industrial and institutional partners
, ensuring solutions meet operational, safety, and compliance requirements
What you will do
Lead AI delivery
(solution architecture, infrastructure decision and deployment) in projects and scope them and their technical feasibility through client interaction.
Configure foundation models
and define prompting + tool‑use strategies for business workflows.
Evaluate and select the
most appropriate AI providers
and frameworks based on performance, cost, latency, safety and scalability.
Define and implement approaches
for prompt evaluation, hallucination mitigation, guardrails, observability, and human‑in‑the‑loop review where needed.
Deploy and operate GenAI services in production
integrating LLM APIs, vector databases, orchestration frameworks, backend services, cloud infrastructure, and monitoring.
Apply
agentic AI‑assisted coding
to reduce cycle time and improve results across the above responsibilities.
About you
Master’s degree in Computer Science, Engineering, Physics or related; PhD a plus.
Strong hands‑on experience (5+ years)
building and deploying applications
based on deep learning frameworks (e.g. TensorFlow, PyTorch), RAG architectures, task‑specific copilots or multi‑step reasoning systems.
Strong Python skills and solid experience with
agentic AI‑assisted coding
(e.g., Claude Code, Codex),
modern LLM tooling
(e.g., LangChain, LangGraph, ADK) and
API integration patterns
.
Solid data foundations
: SQL and experience with data warehouses and ETL/ELT pipelines; familiarity with vector databases (e.g., pgvector, Qdrant, Redis).
Experience with
evaluation frameworks
: model choice, prompt strategies, retrieval tuning with attention to latency and token/cost usage.
Production‑grade delivery:
Kubernetes + Docker + Git and hands‑on exposure to CI/CD, observability and tracing tools such as OpenTelemetry, Grafana/Prometheus.
Hands‑on experience in
designing
cloud enterprise solutions
for GenAI workloads (e.g., Azure OpenAI/ Bedrock/ Vertex AI) with at least one hyperscaler stack (Azure/ AWS/ etc.), including deployment, monitoring and cost control.
Familiarity with
multiple AI providers
and model ecosystems, including commercial APIs and/or open‑source models.
Proficiency in
Italian
and
English
.
Nice to Have
Experience with
fine‑tuning
strategies.
Experience with
guardrails
,
safety
tooling, and/or AI compliance (e.g. AI Act, internal AI governance).
Experience with
edge analytics
or deployment in constrained industrial environments.
Broad software engineering background, including experience with at least
another programming language
(e.g. Java, Typescript, C/C++),
NoSQL databases
, distributed and cloud‑native systems.
Knowledge of
frontend integration
for AI assistants/copilots.
We encourage you to
apply even if you do not believe you meet every single qualification
. Not all strong candidates will meet every single qualification as listed.
What we offer
Access to our
advanced computing infrastructure
A team
with engineers and researchers working together on real industrial AI deployments
Chance to co‑author papers
for top‑tier conferences like NeurIPS, ICML, CoRL, and RSS
Exposure and collaboration with a
huge network
of corporates, SMEs, startups and technology partners
An exceptional workplace @OGR, Turin at the epicenter of tech
Lots of
learning opportunities
: IAS, internal Academy, budget for events, conferences and online courses
Relocation incentives
and competitive benefits package (including potential tax advantages for international candidates, where applicable)
Selection process
Fit interview
Technical live assessment
Final motivation interview
How to apply
Submit your application exclusively through the online form including:
CV
Cover letter (max. xlwpduy 1 page) describing how your profile fits the position and
personal projects
, if any
Optional, links to code repositories (e.g., GitHub), personal webpage or projects (e.g., open‑source contributions)
Optional, names of two references
Note: to ensure a timely process, we will only be in touch with candidates who progress to the interview stage.
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