Janus Health · 2 days ago

Lead Machine Learning Engineer

Chicago, IL

Full-time

Onsite

Senior Level

5+ years exp

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Responsibilities

Collaborate with Data Scientists and product teams to understand user stories and convert them into technical requirements for machine learning solutions.

Manage, mentor, and provide technical guidance to junior team members, promoting knowledge sharing and professional growth.

Design, build, and deploy production machine learning systems on AWS cloud infrastructure.

Develop and maintain robust data and machine learning pipelines, ensuring scalability, reliability, and efficiency.

Implement and optimize monitoring, logging, and alerting systems to ensure the health and performance of deployed machine learning models.

Work closely with DevOps and infrastructure teams to ensure seamless integration and deployment of machine learning pipelines.

Automate and streamline the end-to-end machine learning workflow, including data ingestion, feature engineering, model training, evaluation, and deployment.

Implement best practices for version control, model reproducibility, and model deployment.

Collaborate with cross-functional teams to identify and address performance bottlenecks, scalability challenges, and data quality issues.

Continuously evaluate and adopt emerging technologies and tools to improve the efficiency and effectiveness of machine learning operations.

Qualification

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Machine LearningAWSPythonSQLLinuxDockerKubernetesPySparkAirflowSagemakerMLOpsData PipelinesProblem-SolvingCommunicationComputer VisionNLPReinforcement LearningHealth SystemHealthcare TechnologySoftware as a ServiceSaaS

Required

5+ years of relevant work experience in ML, including designing, building, and deploying production machine learning systems.

Strong expertise in deploying machine learning models on AWS cloud infrastructure.

Proficiency in Python and SQL for data manipulation, analysis, and model development.

Strong knowledge of Linux, Docker, and Kubernetes for containerization and orchestration of machine learning applications.

Strong knowledge of PySpark, Airflow, and data pipelines; strong knowledge of Sagemaker and machine learning pipelines.

Passion for learning and staying updated with the latest advancements in machine learning, MLOps, and cloud technologies.

Excellent communication skills, with the ability to effectively communicate complex technical concepts to both technical and non-technical stakeholders.

Bachelor's degree in Computer Science, related field, or equivalent experience.