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 cross‑functional 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 cloud‑based 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.
Track record of ownership and problem solving—driving measurable impact and quality under ambiguity and evolving requirements.
Ability to communicate technical decisions clearly and contribute to documentation and design discussions.
Demonstrated system design & architecture skills for scalable, high‑performance ML services and batch/streaming workflows; familiarity with API design and service integration patterns.
Proven understanding of tradeoffs in latency, cost, performance, and compliance.

Machine Learning Engineer

Key skills