Lead AI Engineer

About the Position

DOS MIND, DOS Design S.p.a., is seeking a Lead AI Engineer to guide the design and evolution of our GenAI systems, client solutions, and internal tooling. This role involves working with Discovery competence areas to develop new products. As a technical leadership position, the selected candidate will define standards, make architectural choices, and lead the team's development and growth.

Responsibilities

• Define the architecture of GenAI systems: make/buy choices, model selection, cloud vs on-premise trade-offs based on data, cost, and performance constraints
• Provide technical leadership for complex projects, from design to production, ensuring scalability and reliability
• Design and manage self-hosted serving of open-weight LLMs (vLLM)
• Define quality and LLMOps standards: evaluation, observability, testing, cost management, versioning of prompts and models
• Implement guardrails and security: mitigation of prompt injection, PII management, output controls
• Mentor junior and mid-level profiles, guiding code reviews and design reviews
• Act as a technical interface with clients and internal stakeholders, contributing to product decisions and technology scouting

Requirements

• Proven experience (5+ years) in software or ML engineering, with at least 2 years in the development and operation of GenAI/LLM systems in production
• Solid and demonstrable experience in implementing agentic systems in production and engineering complex RAG architectures at scale (multi-tenant environments, high traffic volumes, and sensitive data management)
• In-depth technical knowledge of at least one hyperscaler platform (AWS, Azure, GCP) and solid expertise in hybrid or on-premise deployment solutions
• Proficiency in serving and optimizing open-weight models (via vLLM or equivalent frameworks), including quantization, batching, and advanced GPU resource management
• Strong methodological foundations related to distributed system architectures, Kubernetes, networking, and cybersecurity protocols
• Full mastery of advanced evaluation methodologies and major observability/LLMOps frameworks (such as Langfuse or LangSmith, with automated evaluations and human-in-the-loop processes)
• Proven experience in a technical leadership role, including mentoring activities, setting quality standards, and coordinating multidisciplinary project teams

Nice to Have

• Proven experience in implementing production-level solutions for classical machine learning (computer vision, predictive models) or in data engineering
• Advanced MLOps (Kubeflow management and CI/CD pipeline development for the model lifecycle)
• Experience in regulated environments or contexts subject to strict data residency constraints
• Active contributions to open-source projects, industry conference presentations, or scientific publications in the AI/ML field

We Offer

• Competitive salary
• Opportunity to work in a hybrid model (part remote, part on-site)
• Professional growth and development opportunities

About the Company

DOS is a design company with over 100 professionals, including designers, facilitators, and researchers, committed to producing a positive impact in large organizations.

Application Process

• First individual exploratory interview: This can be conducted in person or remotely. You will meet with our People Management Process Area to assess your soft skills, relational aspects, character traits, motivations, and alignment with our values.
• Second individual technical interview: Preferably in person. You will meet with the Head of the Process Area you will be part of. We will delve into your hard skills and take the time to discuss our projects. You may be asked to perform technical exercises related to your skill set.
• Third individual in-depth interview: This is exclusively in person and is the final stage of the selection process. It is important for us to further explore your hard and soft skills and to show you your development and training path at DOS. It is important for you to share your expectations regarding economic and organizational aspects.