Machine Learning Engineer Jobs in SoundThinking Inc Fremont, CA

We’re seeking a hands-on Machine Learning Engineer (MLOps / ML Infrastructure Engineer who blends the best of DevOps, Data Engineering, and Machine Learning Engineering). Your mission will be to deploy ML models, build robust CI/CD pipelines, and maintain a scalable data and ML infrastructure leveraging AWS, Databricks, and Amazon Redshift.

Essential Responsibilities:

• Model Deployment: Design and implement scalable deployment workflows for machine learning models using tools like MLflow, SageMaker, or Databricks ML.
• CI/CD Automation: Build and maintain end-to-end CI/CD pipelines for model training, validation, and deployment using tools such as GitHub Actions, GitLab CI, or Jenkins.
• Cloud Infrastructure: Architect and manage ML infrastructure on AWS, including EC2, S3, Lambda, EKS, SageMaker, and Redshift.
• Databricks Operations: Maintain Databricks workspaces, develop PySpark jobs, manage clusters, and integrate ML workloads with data pipelines.
• Data Pipelines: Build robust data ingestion and transformation pipelines from Redshift and other sources to serve model training and inference workflows.
• Monitoring & Observability: Implement systems for tracking model performance, data drift, and pipeline health using CloudWatch, Databricks metrics, or custom dashboards.
• Automation & Orchestration: Use tools like Airflow, Prefect, or Databricks Jobs to automate workflows for training and batch inference.
• Data Governance: Ensure data workflows and ML pipelines adhere to governance, lineage, access control, and compliance policies across environments.

Minimum Qualifications:

• 3+ years of experience in MLOps
• Degree in Computer Science, or Engineering discipline required
• Strong proficiency in Databricks and PySpark
• Hands-on with AWS services (S3, Redshift, Lambda, SageMaker, IAM, CloudFormation/Terraform)
• Proven experience with AI model deployment pipelines in production environments
• Solid experience in building CI/CD pipelines