The Sr. Machine Learning Engineer is responsible for designing, building, and deploying machine learning systems that power AI-driven features across Mirion's products. This role combines hands-on modeling and ML infrastructure work with technical leadership — driving best practices for the ML lifecycle, mentoring engineers, and partnering with stakeholders to translate business problems into production-grade ML solutions.

Responsibilities

• Design, train, and deploy machine learning models for applied use cases across radiation safety, nuclear energy, and nuclear medicine.
• Architect end-to-end ML systems, including training pipelines, model serving infrastructure, and monitoring.
• Lead technical design reviews and mentor junior ML engineers on modeling, MLOps, and architectural best practices.
• Establish standards for model evaluation, experiment tracking, reproducibility, and responsible AI across the team.
• Partner with the Data Platform team to define feature requirements and ensure ML workloads are well-supported by the underlying data infrastructure.
• Collaborate with stakeholders and product partners to translate business problems into well-scoped ML solutions.
• Drive optimization initiatives for model performance, inference cost, and reliability in production.
• Participate in hiring and team building for the Applied AI function.
• Contribute to architectural decisions and long-term ML strategy.
• Troubleshoot production model issues — drift, degradation, and pipeline failures — and implement robust monitoring and alerting.

Minimum Qualifications

• 5+ years experience in machine learning engineering, applied ML, or related field.
• Strong proficiency in Python and modern ML frameworks (PyTorch, TensorFlow, or similar).
• Deep experience taking ML models from research/prototype through to production deployment.
• Hands-on experience with ML infrastructure — training pipelines, model serving, experiment tracking, and monitoring.
• Solid software engineering fundamentals: testing, code review, version control, and CI/CD.
• Working knowledge of SQL and modern data warehouses or lakehouses (Snowflake, BigQuery, Databricks, etc.).
• Experience with cloud platforms (AWS, GCP, or Azure) at scale.
• Proven ability to mentor and guide junior engineers.

Preferred Qualifications

• Experience building applied AI products or ML platforms from the ground up.
• Experience with Databricks, MLflow, and lakehouse-based ML workflows.
• Expertise with LLMs, RAG systems, or generative AI applications in production.
• Experience with feature stores, vector databases, and real-time inference architectures.
• Knowledge of model governance, model lineage, and responsible AI practices.
• Background in regulatory-heavy industries or complex compliance requirements.
• Experience with infrastructure-as-code and MLOps practices.
• Background in computer vision, time-series, or signal processing (relevant to radiation detection data).