Machine Learning Operations Engineer

Qualifications

• Bachelor's or Master's degree in Engineering or a related field.

• MLOps tools such as DVC and Clear ML

• CI/CD and cloud knowledge

• Some Computer Vision experience or knowledge

• Strong organisational skills

• Excellent problem-solving skills and the ability to troubleshoot and resolve issues.

• Strong communication and collaboration skills to work effectively in a multidisciplinary team environment.

• Adaptability to work in a fast-paced, dynamic startup environment with a strong drive for innovation and continuous improvement.

• Ability to travel as needed.

About Us

STARK is a new kind of defence technology company revolutionizing the way autonomous systems are deployed across multiple domains. We design, develop and manufacture high performance unmanned systems that are software-defined, mass-scalable, and cost effective. This provides our operators with a decisive edge in highly contested environments.
We're focused on delivering deployable, high-performance systems—not future promises. In a time of rising threats, STARK is bolstering the technological edge of NATO Allies and their Partners to deter aggression and defend Europe—today.

Your mission

Designing, implementing, and maintaining the infrastructure and workflows that enable the efficient deployment, monitoring, and scaling of machine learning models.

Responsibilities

• Design, implement, and maintain data pipelines for processing large volumes of data, ensuring efficient data flow for model training, testing, and inference.

• Build and manage automated Continuous Integration and Continuous Deployment (CI/CD) pipelines for model training, deployment, and monitoring, enabling seamless transitions from development to production.

• Work closely with data scientists, software engineers, and IT teams to ensure smooth integration of machine learning models and data pipelines into production systems.

• Stay current with emerging technologies and industry trends, recommending and implementing innovations to improve our products and processes

• Dive deep into the details to identify, understand, and solve difficult technical problems.

• Constantly be challenging requirements to determine what adds value and what is not.