(Senior) Machine Learning Engineer - Data Platform

Our requirements

• Have solid, production-proven experience in ML/AI ecosystems and building stable ML-driven solutions.
• Are proficient in Python, know libraries from the MLE’s toolchain (scikit-learn, PyTorch, Pandas, FastAPI) and easily use IDEs.
• Are open to expanding coding skills into JVM languages (Java/Kotlin) or frontend (React/TypeScript) to work efficiently with other parts of our multi-product system.
• Have practical experience with Docker and K8s.
• Have good analytical skills and know SQL and NoSQL.
• Know and apply DevOps / GitOps principles in your work.
• Are familiar with cloud ecosystems (Google Cloud Platform preferred) and modern infrastructure tools (Kubernetes, Helm, ArgoCD, Terraform).
• Have practical experience with GPU/TPU usage in model training, fine-tuning, and distributed AI and ML model inference (KServe, Ray Serve / NVIDIA Triton Inference Server / TorchServe), along with full model lifecycle management.
• Advocate for engineering excellence, including clean code, TDD, CI/CD, and a strong commitment to rigorous code reviews to maintain team architectural stability.
• Practice in monitoring and knowledge of Prometheus, Grafana, and OpenTelemetry concepts.
• Understand and have experience with AI ecosystem components.
• Are enthusiastic about utilizing AI coding tools to automate repetitive tasks and optimize your daily workflow.
• Have a positive attitude, excellent teamwork skills, and a higher technical education (Computer Science or related fields).
• Speak Polish at a minimum C1 level and English at a minimum B2 level.

Optional

• Experience with Open-weight LLMs fine-tuning, and performance optimization.
• Experience with LLMOps deployment, evaluation, observability, and tracing tools.
• Experience with AzureML, MLflow, modern SQL-based data transformation frameworks like dbt.
• Advanced AI: Agentic Engineering, Harness, Expertise in LangGraph, ADK (multi-agent systems), and AI Guardrails.

Your responsibilities

• MLOps Ecosystem: Developing a modern MLOps ecosystem, automating, and maintaining pipelines for ML model training and serving, focusing on high-performance.
• AI Enablement: Building the Allegro AI ecosystem, including AI Gov inventory, AI-Gateway agentic workflows, runtime, model Guardrails, and evaluation frameworks.
• Support: Supporting internal partners and product teams to build and maintain robust ML/AI-based microservices.

About the project

#goodtobehere means that:

• You will join a team you can count on - we work with top-class specialists who have knowledge- and experience-sharing in their DNA.
• You will love our level of autonomy in team organization, the space for continuous development, and the opportunity to try new things. You get to choose which technology solves the problem and you are responsible for what you create.
• You will value our Developer Experience and the full platform of tools and technologies that make creating software easier. We rely on an internal ecosystem based on self-service and widely used tools such as Kubernetes, Docker, Consul, GitHub, and GitHub Actions. Thanks to this, you can contribute to Allegro from your very first days on the job.
• You will be equipped with modern AI tools to automate repetitive tasks, allowing you to focus on developing new services and refining existing ones (also leveraging AI support).
• You will create solutions that will be used (and loved!) by your friends, family and millions of our customers.
• You will meet the Allegro Scale, which starts with over 1000 microservices, an open-source data bus (Hermes) with 300K+ rps, a Service Mesh with 1M+ rps, tens of petabytes of data, and production-used machine learning.
• You will become part of Allegro Tech - we speak at industry conferences, cooperate with tech communities, run our own blog (it's been over 10 years!), record podcasts, lead guilds, and we organize our own internal conference - the Allegro Tech Meeting. We create solutions we love (and can) to talk about!

What we offer

• Flexible working hours in the hybrid model (4/1) - working hours start between 7:00 a.m. and 10:00 a.m. We also have 30 days of occasional remote work.
• Long term discretionary incentive plan based on Allegro.eu shares (restricted stock units).
• Annual bonus based on your annual performance and company results.
• Well-located offices (with e.g. fully equipped kitchens, bicycle parking, terraces full of greenery) and excellent work tools (e.g., raised desks, ergonomic chairs, interactive conference rooms).
• A 16" or 14" MacBook Pro or corresponding Dell with Windows (if you don't like Macs) and all the necessary accessories.
• A wide selection of fringe benefits in a cafeteria plan - you choose what you like (e.g., medical, sports or lunch packages, insurance, purchase vouchers).
• English classes that we pay for related to the specific nature of your job.
• A training budget, inter-team tourism (see more here), hackathons, and an internal learning platform where you will find multiple trainings.
• An additional day off for volunteering, which you can use alone, with a team, or with a larger group of people connected by a common goal.
• Social events for Allegro people - Spin Kilometers, Family Day, Fat Thursday, Advent of Code, and many other occasions we enjoy.

Benefits

• sharing the costs of sports activities
• private medical care
• sharing the costs of foreign language classes
• sharing the costs of professional training & courses
• life insurance
• flexible working time
• integration events
• no dress code
• leisure zone
• extra social benefits