- As a Machine Learning Engineer, you’re a highly motivated individual with strong fundamentals in computer science and hands-on experience across the full model development lifecycle—including feature engineering, model development, calibration, deployment, and ongoing monitoring.
- In this role, you will be flexible, eager to learn new skills, and willing to contribute wherever the team needs support.
- This Machine Learning Engineer is comfortable working with both traditional tabular machine learning models and modern AI techniques, including prompt engineering and LLM-based capabilities.
- Develop and deliver end-to-end machine learning solutions, including defining technical requirements, architecting scalable systems, and implementing monitoring, logging, and maintenance workflows.
- Collaborate closely with engineers, product managers, clinicians, and crossfunctional partners to build new ML products and enhance existing systems.
- Lead the design and implementation of MLOps frameworks, including pipeline development, CI/CD integration, drift detection, retraining workflows, and rollback strategies.
- Monitor model performance in production, identify issues, propose remediation steps, and ensure strong test coverage and system reliability.
- Utilize contemporary software engineering practices to implement scalable, secure, and maintainable AI/ML systems.
- Develop and customize API integrations to enable seamless connectivity between cloudbased systems and ML services.
- Participate in architectural discussions to ensure ML platforms meet compliance, performance, and scalability standards.
- Bachelor’s degree in Computer Science, Data Analytics, Software/Computer Engineering, Computational Statistics, Mathematics, or a related discipline.
- 3+ years of end-to-end ML development in production (data prep, feature engineering, modeling, calibration, deployment, monitoring, maintenance).
- 3+ years of MLOps experience building production pipelines (CI/CD, model registry, feature store), implementing monitoring & drift detection, and automating retraining.
- 3+ years of Python for production ML (testing, packaging, type hints, linting) and SQL for analytical and production workloads; Scala a plus.
- 2+ years working with distributed compute and cloud ML environments (e.g., Spark/Databricks on Azure/AWS/GCP) and modern data ecosystems (data lakes, DBMS).
- Strong debugging and optimization skills across data and ML workflows.
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