Senior Machine Learning Engineer at NYALA

Lisbon, , Portugal -

Full Time

Start Date

Immediate

Expiry Date

23 Sep, 26

Salary

0.0

Posted On

25 Jun, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, Generative AI, Terraform, Python, Cloud Platforms, CI/CD, LLM Operations, MLOps, System Design, Infrastructure As Code, Model Registry, Experiment Tracking

Industry

Description

Function Description / Professional Tasks You'll join the internal Data & AI Engineering platform team, responsible for the tooling and components that enable data and AI use cases across the company. The team builds on top of existing cloud infrastructure - owning the data and AI layer: reusable modules, lifecycle tooling, agentic components, and the internal developer experience for teams building on the platform. You'll contribute to design, own implementation, and work directly with internal teams that consume what you build. What you can do − Building and maintaining reusable platform components: Terraform modules, deployment blueprints, and abstractions that internal ML and GenAI teams depend on − ML lifecycle tooling: experiment tracking, model registry, deployment pipelines, monitoring − GenAI-specific components: LLM access and routing, agent lifecycle and versioning, tool integrations, guardrails, evaluations − CI/CD for ML and GenAI workloads − Supporting internal teams in adopting platform components - documentation, examples, hands-on guidance where needed Personal Skills and Education What you bring − You have more than 5 years of proven experience working with AI. − Solid engineering fundamentals - maintainable code, clear system design thinking, ability to own deliverables end-to-end − Cloud platform experience (AWS, GCP, Azure, or Databricks). − Infrastructure-as-code experience; Terraform exposure is a plus − Python proficiency − Experience building and maintaining ML workflows end-to-end - training pipelines, experiment tracking, model registry, deployment, and monitoring. − Familiarity with relevant tooling (e.g. MLflow, SageMaker Pipelines, Kubeflow, or similar). − Hands-on experience with LLMs or GenAI tooling in a technical context - working with model APIs, building or operating agentic systems, or contributing to GenAI infrastructure (evaluation, observability, guardrails, etc.) Education − University/technical college degree in computer science or comparable. Language Skills − English oral and written − Optional: German oral (and written) Additional Comments − Willingness to travel − Hybrid Model: One day a week @office (mandatory) Why us? Just by joining us you will get benefits like: Open minded company where every employee has to contribute to the development of the company - ideas are welcome as well as independent thinking to 25 annual days of vacations Flexible working hours Annual allowance for Benefits (Training, Gym, Public Transportation, Technologies, etc...) An amazing onboarding week at Switzerland iPhone Second Screen to work at home (Flat or a Curved one) First month Tech Allowance to buy your headset or, if you already have one, whatever you need to work comfortably Health insurance for you and your family Life insurance Office Perks (coffee, fruit, stand up desks. etc...) So much more... About us At the IT Campus, you can grow thanks to challenging IT projects, flat hierarchies, an inspiring environment and state-of-the-art technologies. You also have the opportunity to build up the IT Campus from scratch. You can work flexibly in terms of time and location and benefit from attractive working conditions. Swiss Post connects people, brings cultures together and is one of the largest IT employers in Switzerland: We handle over 400 software projects each year and run more than 1,000 applications – with these figures set to rise in future.

Responsibilities

Develop and maintain reusable platform components, including Terraform modules and deployment blueprints, to enable AI use cases across the company. Build and manage ML lifecycle tooling, GenAI-specific components, and CI/CD pipelines for internal developer experience.