Forward-Deployed AI Engineer

We have multiple positions to fill. The type of position

we offer
depends on your experience and

qualifications:
• No work experience, with a Bachelor's or Master's degree:

we offer
you an internship.
• At least 2 years of work experience, or a Ph.
D. / postdoc:

we offer
you a full-time position.

About Us
We are an AI startup founded in 2020, headquartered in Dublin, Ireland, with an AI Lab in Milan, Italy. Our expertise spans machine learning and generative AI for financial services. At Gemmo AI, we build custom AI solutions that combine automation with human insight. We use a modular approach: first we explore the highest-impact opportunities, then we design and deploy tailored solutions, and finally we help improve and maintain them over time. We believe in responsible, pragmatic AI: systems that integrate into real workflows, provide measurable value, and remain under the client's control. Our Team
• Forward Deployed Engineers: 25 people, including 4 Ph.
D.s
• Leadership: 4 people We are a lean, technical-first team, and we're growing. We're looking to add 17 new Forward-Deployed AI Engineer roles before the end of the year. The Role A forward-deployed engineer works directly with the client and owns the solution end to end: from understanding the business problem, to choosing and building the right model, to shipping and running it in production. There's no handoff chain. The same engineer who hears the problem delivers the answer. In practice, the role combines three disciplines:
• Business analysis: understanding how a financial institution works, mapping its processes, and turning a vague request into a clear specification.
• ML engineering: building, evaluating and improving models, from classic prediction models to fine-tuned LLMs.
• Software engineering: building the APIs, integrations and applications that put models into real workflows, then deploying and maintaining them. Nobody arrives strong in all three. We hire people who are solid in at least one and eager to grow into the others.

What You'll Do
You'll work on AI solutions for one of the most data-rich industries in the world, in direct contact with enterprise clients. Depending on the project, you might:
• Sit with client stakeholders to understand a workflow and identify where AI creates measurable value
• Build prediction models and ML-based document analysis
• Fine-tune LLMs to power conversational interfaces over the client's data
• Extract actionable insights from large-scale datasets
• Design the APIs and integrations that connect your models to the client's systems
• Deploy, monitor and improve what you've built once it's live Both tracks involve close collaboration with senior engineers and direct exposure to enterprise clients. This is not a support role: you'll be expected to contribute from day one. What You Will Learn
• Running discovery with clients and translating business problems into technical plans
• Building and evaluating ML and generative AI solutions for financial services
• Designing, building and maintaining CRUD APIs to interact with users and serve models
• Deploying, monitoring and maintaining applications in Azure and Snowflake
• Communicating technical work clearly to non-technical stakeholders Who You Are
• You have a solid foundation in machine learning or software engineering, from your studies, research or work
• You enjoy understanding why something needs to be built, not just how
• You're comfortable talking to clients and asking questions when a problem is unclear
• You take ownership of outcomes, not just tasks
• You write clearly, because most of our communication happens in writing
• Interest in financial services is a plus; prior experience is not required Tech Stack We use a mix of modern tools and languages. You'll have the chance to explore and work with technologies like these:
• Languages: Python, SQL
• ML frameworks: PyTorch, XGBoost
• API frameworks: FastAPI
• Databases: Snowflake, Postgres
• Cloud: Azure, AWS How We Work Everyone is expected to communicate clearly, move fast, and take ownership. Communication. We run one short standup every morning to align on daily priorities. Everything else lives in writing: project documentation on Linear and GitHub, and async conversations on Slack, with dedicated channels per team and project. Rhythm and organisation. We work in weekly sprints, so priorities are always clear and nothing drags. Every Friday we run a retrospective: an open conversation about what's working and what isn't. Compensation: Internships
• Compensation: €800 /month with a Bachelor's, €1,000 with a Master's
• Contract type: Convenzione Stage curriculare / extra-curriculare
• Food allowance: €120/month
• Travel reimbursement: up to €370 per month
• Equipment: brand-new Apple M5 14-inch
• Non-cash value: mentorship and fast promotion
• Duration: 6 months, with a 1-month trial period Career Path for Interns This internship is not a dead end. It's the front door. We hire interns with the explicit intention of converting them into full-time forward-deployed engineers, and most interns move to a permanent contract within 6 months. Compensation: Full-Time
• RAL: €30,000–40,000 gross, CCNL Metalmeccanico level C3 / C2 depending on experience
• Food allowance: €1,200
• Salary progression: 5–10% each year, subject to performance review
• Salary reviews: every year
• Equipment: brand-new Apple M5 14-inch Remote Work and Schedule
• 3 days in the office (Tuesday, Wednesday, Thursday) and 2 days (Monday, Friday) working from anywhere
•

Working hours
Monday to Friday, 8:30–17:30 CET
• Lunch: 13:00–14:00 (flexible) Selection Process We keep it fast, respectful and transparent.
• Interview with the CEO (15 min): motivation, background knowledge and availability
• Interview with the CTO or a Senior Engineer (15 min): company and role presentation, alignment on expectations
• Technical interview (30–40 min): a technical discussion on ML principles and system design. No whiteboard coding or LeetCode-style questions. Travel: Once a year, the whole team flies to Dublin for a 3-day offsite at our HQ. Offsite: We also invite once a year the company to a nice place