FDE / ML Engineer

We are looking for talents who:

Pyxis is looking for someone to join our ML & AI team.

If you closely follow the challenges the industry is facing and see them as opportunities rather than problems, if you’re comfortable with uncertainty and want to grow on a team that supports you through it, we’d love to hear from you!

Responsabilities:

In this role, you’ll take an active part (with the opportunity to lead) in AI implementation and adoption projects for clients across a range of industries, as well as internal projects. Our goal is to maximize the value AI delivers by combining technical depth with a real understanding of the business and how each organization works.

A key part of the role is contributing to our active technology watch practice: exploring and applying cutting-edge technologies to strengthen our own skills, the team’s, and the company’s, and to sustain our competitive edge.

Requirements:

• 5+ years of experience as an ML Engineer, FDE, or in similar roles, combining solid Machine Learning knowledge with direct client work.
• At least 2 years of experience building LLM-based solutions deployed to production, including RAG systems, agent architectures, and LLM integration into applications.
• Experience working across the full ML model lifecycle, backend development, data pipelines, and monitoring and evaluation of solutions in production.
• Ability to understand the client’s business and processes, identify and prioritize AI opportunities, and implement them end to end.
• Python as your main language.
• Experience with technologies such as LangGraph, PydanticAI, or similar; vLLM, LiteLLM, or similar; FastAPI or similar; AWS or Azure; Docker and Kubernetes.
• Advanced English.

Nice to have:

• Technical leadership of projects or teams, and/or mentoring of junior team members.
• Pre-sales and technical proposal writing.
• Public sector or regulated industries.
• Deploying and operating solutions on the client’s cloud or on-premise infrastructure.
• Fine-tuning and serving open-source models.
• Open-source contributions, publications, or research experience.