Machine Learning Engineer

Mission Overview:

As a Keystone Solutions consultant, you will be deployed on a consultancy mission at the client, delivering robust, scalable, and maintainable machine learning solutions integrated into existing systems and data flows in both Azure cloud and on-premise environments. The client is a dynamic organization with an informal culture and a bilingual (Dutch and French) environment. Within IT, Agile SAFe (Scaled Agile Framework) is used and development teams are multidisciplinary. You will ensure models are not only performant in experiments but also reliable and manageable in production, with strong attention to reproducibility, monitoring, regulatory compliance (including the European AI Act), and continuous improvement.

Consultancy Nature of Work:

Under the Keystone Solutions consultancy model, you will work closely with the client's stakeholders on site and remotely, embedding within multidisciplinary teams to translate ML use cases into sustainable technical implementations. As a Keystone Solutions consultant, you will align with client processes (including Agile SAFe) while adhering to Keystone's engineering standards.

Dynamic Projects:

Engage with diverse challenges across client projects, from data preparation and feature engineering to deployment and monitoring of ML services in Azure and on-premise contexts. This role offers exposure to a variety of use cases such as classification, regression, forecasting, detection, and scoring.

Turbo-Charged Learning and Development:

Benefit from broad learning experiences through hands-on consultancy work, pairing with data engineers, developers, and architects. Keystone Solutions supports your growth in MLOps, CI/CD, monitoring, and reliable ML delivery through real-world client engagements.

Ambition Skyrocketing within a Consultancy Framework:

Keystone Solutions fosters career growth by enabling consultants to take ownership of technical implementations, propose improvements proactively, and strengthen communication with both technical and non-technical stakeholders across client organizations.

Keystone Solutions' Values in Action:

Being a K-Stone means bringing excellence, collaboration, clarity, and continuous learning to every client mission. You will uphold quality, reliability, and maintainability standards while contributing to best practices in ML engineering, testing, deployment, and monitoring.

Role Title:

Confirmed Machine Learning Engineer (Consultancy mission via Keystone Solutions)

Key Responsibilities:

• Design, build, deploy, and maintain machine learning models and ML pipelines at the client. Ensure models operate reliably, scalably, and manageably in production across cloud infrastructures (Azure) and on-premise.
• Work closely with data engineers, developers, architects, and business stakeholders to translate ML use cases into sustainable technical implementations compliant with applicable regulations (notably the European AI Act).

Data Preparation and Feature Engineering:

• Process, analyze, and prepare data from various internal and external sources.
• Design and implement data transformations and feature engineering processes.
• Safeguard data quality, consistency, and reproducibility within ML workflows.
• Collaborate with relevant teams to make data reliably and reusablely available for ML use cases.

Model Development and Validation:

• Design, train, test, and tune machine learning models for use cases such as classification, regression, forecasting, detection, or scoring.
• Select appropriate techniques and evaluation methods based on the use case and production context.
• Run experiments and benchmark models with attention to quality, explainability, and maintainability.
• Define clear validation criteria for models before they are moved into production.

Operationalizing ML Solutions:

• Translate models and experiments into production-ready services and pipelines.
• Integrate models into backend services, APIs, or batch processes.
• Implement version control for code, configuration, models, and relevant datasets.
• Contribute to a standardized and reliable deployment approach for ML solutions.

MLOps, Monitoring, and Reliability:

• Set up and maintain ML pipelines, CI/CD processes, and release approaches for ML components.
• Provide monitoring for performance, stability, latency, error handling, data drift, and model drift.
• Develop retraining and feedback mechanisms to keep models current and performant.
• Safeguard reliability, scalability, cost control, and operational manageability of ML solutions.

Collaboration and Knowledge Sharing:

• Align with developers, data engineers, architects, and business stakeholders on technical choices and implementation.
• Contribute to good practices around ML engineering, testing, deployment, and monitoring.
• Document implementations, assumptions, and operational considerations.
• Share knowledge with teams and actively contribute to the maturity of ML within the organization.

Behavioral Competencies:

• Result-oriented and pragmatic: able to translate ML solutions into stable and usable production components.
• Strong analytical and logical reasoning skills.
• Quality-conscious, with attention to reliability, maintainability, and clarity.
• Ownership of technical implementations and proactive in proposing improvements.
• Communicative: able to clearly explain technical choices to both technical and non-technical stakeholders.
• Strong in collaboration within multidisciplinary teams.
• Eager to learn and motivated to apply new techniques and best practices in a production context.

Language Skills:

• Dutch or French (native level).
• Understanding of the second national language.

Work Regime:

• Hybrid: typically 2 days per week on site at the client and 3 days remote.

Required Skills and Proficiency Levels:

• Python (data and ML development): Confirmed.
• Azure: Junior (nice to have).
• C# / .NET / Blazor Framework: Junior.
• CI/CD, version control, and deployment of ML services: Junior.
• Containerization and deployment patterns (Docker): Junior.
• Data preparation, feature engineering, and model validation: Junior.
• Experiment tracking, model registry, or workflow orchestration: Junior.
• Integration of ML components into applications or backend services: Junior.
• Machine learning libraries and open-source models/tools (scikit-learn, PyTorch, Langraph, Ollama, LangChain): Junior.
• ML pipelines and MLOps practices (Azure DevOps): Junior.
• Monitoring of models and pipelines (logging, metrics, drift detection, OpenTelemetry, Dynatrace): Junior.
• SQL and data processing in a production context: Junior.

Environment and Methodology:

• Bilingual stakeholder environment (French and Dutch) with an informal culture.
• Agile SAFe methodology with multidisciplinary development teams.
• Solutions deployed across Azure and on-premise environments.

What You Will Do as a Keystone Solutions Consultant at the Client:

• Execute all responsibilities listed above under Keystone's consultancy model, ensuring production-grade ML services with versioning for code, configuration, models, and datasets.
• Collaborate closely with client teams to make data reliably available for ML use cases and integrate ML into backend services, APIs, or batch processes.
• Implement CI/CD, monitoring for performance and drift, and retraining mechanisms to maintain model quality and compliance with the European AI Act.
• Document assumptions and operational considerations and contribute to the client's ML maturity while upholding Keystone's standards.

Why Join Keystone Solutions as a Consultant:

• Apply your machine learning expertise across varied client scenarios, enhancing your technical breadth and depth.
• Work within Agile SAFe teams, strengthening collaboration and delivery practices in real production environments.
• Grow your career through ownership, clear communication, and continuous improvement embedded in every mission.

If you are ready to tackle technical and strategic challenges in a dynamic consultancy environment, apply today at Keystone Solutions Career Portal.