Production ML & .NET Engineer: Build Scalable AI APIs

AdvanceWorks; Lisboa; Hybrid; EUR 55,000 - 85,000; Full time

Benefits offered by this job

Mentorship program

Formal training and certifications

Hybrid work (3 days per week in Oeiras

Job summary

AdvanceWorks is seeking a Machine Learning Engineer to join a cross-functional team in Lisbon with collaboration across Data Science, DevOps, and Platform groups.

You will productionize ML models, build scalable APIs and inference services, and contribute to reusable ML frameworks. The role spans data processing, model training, evaluation, deployment, and production ops in cloud environments.

Qualifications

• Professional experience in software development and machine learning engineering.
• Strong programming skills in an OO language such as C# or Java and proficiency with Python.
• Familiarity with ML frameworks (TensorFlow, PyTorch) and data processing tools (Spark, Beam, Flink).
• Experience with cloud platforms (Azure, AWS, GCP) and container orchestration (Docker, Kubernetes).
• Understanding of ML lifecycle from data prep to deployment and monitoring.

Responsibilities

• Productionize ML models and build robust APIs and inference services.
• Develop frameworks and reusable ML solutions across projects.
• Collaborate with Data Scientists, DevOps, and Platform teams to ensure scalable ML workloads.
• Design end-to-end ML pipelines from data processing to deployment and inference.
• Maintain production ML services and integrate with cloud and data platforms.

Skills

Software development

ML engineering

Python

C# or Java

Education

Bachelor's degree in Computer Science or equivalent

Tools

TensorFlow

PyTorch

Scikit-Learn

Spark

Beam

Flink

Azure

AWS

GCP

Docker

Kubernetes

MongoDB

Redis

SQL

Job description

AdvanceWorks is seeking a Machine Learning Engineer to join a cross-functional team in Lisbon with collaboration across Data Science, DevOps, and Platform groups.

You will productionize ML models, build scalable APIs and inference services, and contribute to reusable ML frameworks. The role spans data processing, model training, evaluation, deployment, and production ops in cloud environments.