Principal ML Engineer

Client of Involved Solutions

We have partnered with a product-led organisation building large-scale, production-grade machine learning systems.

This is a Principal ML Engineer role for someone who wants to set technical direction, solve hard problems and influence how ML is built and deployed across the business.

You will be hands-on where it matters, while also shaping architecture, standards and best practices for ML at scale.

About the role:

• Define and evolve the organisation's machine learning architecture and technical standards
• Lead the design and deployment of scalable ML pipelines and inference systems
• Own the reliability, performance, and observability of models running in production
• Partner closely with data science, engineering, and product leaders on high-impact ML initiatives
• Guide and mentor senior engineers and data scientists on ML engineering best practices
• Drive adoption of modern ML and applied AI techniques where they create real value

About you:

• Deep experience building production ML systems using Python and modern ML frameworks such as PyTorch or TensorFlow
• Strong background in classical ML (scikit-learn, gradient boosting) and feature engineering at scale
• Proven experience working with large datasets using Pandas or Polars and distributed processing frameworks like Spark or PySpark
• Hands-on experience deploying models using Docker, APIs (for example FastAPI) and cloud-native infrastructure
• Strong MLOps experience including experiment tracking, model versioning, monitoring and drift detection using tools such as MLflow or Weights & Biases
• Experience orchestrating ML workflows with Airflow, Prefect or Dagster
• Solid cloud experience across AWS, GCP, or Azure, including scalable compute and managed ML services
• Practical exposure to modern applied AI, including embeddings, vector search and retrieval-augmented generation
• Comfortable setting technical direction, making trade-offs and owning ML outcomes in production