- Company Name
- Lantum
- Job Title
- ML Engineer
- Job Description
-
Job Title: ML Engineer
Role Summary: Design, develop, and deploy end‑to‑end AI models that drive a cloud‑based workforce scheduling platform. Work hand‑in‑hand with engineering to integrate models into production, ensure scalability, reliability, and compliance, and continuously improve data pipelines and experimentation workflows.
Expectations:
- Deliver production‑grade code and machine‑learning models with strong accuracy, fairness, and regulatory compliance.
- Collaborate cross‑functionally with data, engineering, and product teams to translate business requirements into algorithms and system architecture.
- Maintain and optimise continuous‑integration/continuous‑deployment (CI/CD) pipelines for model training, validation, and deployment.
- Lead and mentor junior data scientists, fostering scientific rigor and best practices.
Key Responsibilities:
- Build, optimise and maintain AI models for complex rota scheduling.
- Develop and improve robust data pipelines, workflows, and experimentation processes.
- Embed AI capabilities into core product workflows in partnership with engineering.
- Apply scientific best practice to ensure model accuracy, fairness, and regulatory compliance.
- Contribute to continuous improvement of internal data‑science infrastructure and tooling.
Required Skills:
- Proficient in Python 3, with deep knowledge of data‑science libraries (Pandas, NumPy, SciPy, scikit‑learn, PyTorch/Keras/TensorFlow).
- Experience deploying models to cloud infrastructure (AWS S3, EC2, Lambda, ECS/ECR) and managing model lifecycle (CI/CD, containerisation).
- Strong understanding of optimisation methods, especially constraint‑based scheduling, meta‑heuristics, and OR‑tools.
- Hands‑on experience with CI/CD tools, Git, and adherence to clean coding practices.
- Familiarity with data‑visualisation (Matplotlib, Seaborn, Bokeh).
- Optional: DVC/MLflow/SageMaker, OptaPlanner/TimeFold, Docker, MLOps platforms, SQL/NoSQL databases, Java.
Required Education & Certifications:
- Bachelor’s degree in mathematics, statistics, physics, computer science, engineering, or a related STEM field (Master’s or PhD preferred but not mandatory).
- Formal training or demonstrable experience in descriptive statistics, probability, inferential statistics, software development, and data‑science fundamentals.
- Demonstrated experience delivering production‑grade code in a professional environment.