.NET & Machine Learning Engineer

At AdvanceWorks, we learn together, grow together, and have fun together. And we do all this while creating great software solutions for clients across many industries and geographies.

We are constantly challenging ourselves and each other to use the latest technologies and the best methodologies and make sure we walk the talk.

About the project

You’ll join a technology team working in a collaborating environment closely with Data Scientists, DevOps, Software Architecture, and Platform teams.

The project focuses on bringing machine learning solutions from experimentation into production, building scalable services and reusable frameworks that enable ML solutions to be developed, deployed, and operated efficiently at scale.

You’ll contribute to the full machine learning lifecycle, from data collection and processing strategies to model training, evaluation, deployment, and inference services. You’ll work on production-grade solutions, helping establish common technical frameworks, accelerate the adoption of machine learning practices, and promote the reuse and sharing of solutions across projects and departments.

This is an opportunity to work at the intersection of software engineering, cloud technologies, data engineering, and machine learning, contributing to business-critical solutions in a collaborative and technically driven environment.

What will be your role

We are looking for a Machine Learning Engineer with strong software development skills and a solid understanding of machine learning technologies and production environments.

You’ll be responsible for working closely with Data Scientists to productionize machine learning models, developing robust and scalable APIs and inference services, and contributing to the design of frameworks and platforms that support machine learning solutions at scale.

You’ll participate in the development of end-to-end ML pipelines, working across data processing, model training and evaluation, deployment, and production inference. You’ll also collaborate with DevOps, Software Architecture, and Platform teams to ensure that ML solutions are scalable, reliable, maintainable, and aligned with engineering best practices.

Your responsibilities will include

• Working closely with Data Scientists to bring machine learning services and models into production.
• Designing, developing, and maintaining robust and scalable web APIs to serve machine learning models.
• Developing frameworks and reusable technical solutions for recurring machine learning problems.
• Promoting the adoption of common frameworks and engineering practices that accelerate the delivery of machine learning solutions at scale.
• Contributing to the design and implementation of machine learning pipelines, from data collection and processing strategies to model training, evaluation, deployment, and inference.
• Developing and maintaining production-grade inference services and supporting their integration with other systems.
• Collaborating with DevOps, Software Architecture, and Platform teams to design scalable and reliable ML solutions.
• Working with cloud platforms and cloud-native services to support machine learning workloads and production deployments.
• Contributing to data processing solutions using technologies such as Spark, Beam, or Flink.
• Working with streaming and messaging platforms, particularly Kafka, where applicable to ML and data pipelines.
• Contributing to the automation of infrastructure and processes for training, evaluating, and deploying machine learning models.
• Implementing unit tests and following software engineering best practices throughout the development lifecycle.
• Using Git and established build and release processes to ensure reliable software delivery.
• Contributing to containerized solutions using Docker and Kubernetes.
• Monitoring, troubleshooting, and continuously improving production ML services and supporting infrastructure.
• Contributing regularly to the documentation of systems, frameworks, tools, APIs, and technical processes.
• Promoting knowledge sharing and the reuse of solutions across projects and departments.
• Participating in technical discussions and contributing to architectural and engineering decisions.

What should you bring to the team?

• Professional experience in software development and machine learning engineering.
• Strong programming skills in at least one object-oriented programming language, such as C# or Java.
• Professional experience with Python and familiarity with its use in data science and machine learning environments.
• Familiarity with machine learning frameworks and libraries such as TensorFlow, PyTorch, and/or Scikit-Learn. <