Senior Machine Learning Engineer

Position Summary:

As a Senior Machine Learning Engineer, you will play a critical role in transforming ML models from prototypes to production at scale. You will work closely with data scientists, MLOps engineers, and product teams to design, develop, deploy, and maintain high-performing ML solutions. This role requires a strong blend of engineering, MLOps, and data science skills to ensure continuous, reliable operations in production environments.

Key Responsibilities:

• Model Productionization: Convert ML models from prototypes to scalable, production-ready solutions. Optimize models for performance, scalability, and resource efficiency.
• Integration and Deployment: Develop and maintain pipelines for continuous integration and deployment of ML models. Ensure smooth transitions from development to production.
• Scalability and Optimization: Implement distributed systems and leverage cloud-based architectures to scale ML models. Optimize for low latency and high availability.
• Model Monitoring and Maintenance: Set up monitoring systems to track model performance in production, detect data drift, and trigger automated retraining when needed.
• Innovation and Tooling: Evaluate and integrate new tools, frameworks, and libraries to improve model deployment speed and robustness.
• Documentation and Knowledge Sharing: Maintain well-structured codebases, document processes, promote ML engineering best practices, and lead internal knowledge-sharing sessions.

Qualifications:

• Education: Bachelor's or Master's degree in Computer Science, Machine Learning, Engineering, or a related field.
• Experience: 5+ years in machine learning engineering or software engineering with significant ML focus, including production deployment of ML models.
• Technical Skills:
• Programming in Python
• ML libraries such as TensorFlow, PyTorch, Scikit-Learn
• MLOps tools: CI/CD for ML, containerization using Docker and Kubernetes, workflow orchestration tools like Airflow or MLflow
• Cloud platforms: AWS or GCP, including managed ML services such as SageMaker or Vertex AI
• Distributed computing frameworks: Spark, Dask, and building data pipelines
• Experience with relational databases like MySQL, PostgreSQL; SQL tuning and performance optimization is a plus
• Soft Skills: Strong problem-solving, collaboration, and communication skills. Ability to thrive in a fast-paced, evolving environment and rapidly adopt new tools and technologies.

What You Will Have at Harness:

• Experience building a transformative product
• End-to-end ownership of projects
• Competitive salary and comprehensive healthcare benefits
• Flexible work schedule
• Quarterly Harness TGIF-Off / 4 days
• Paid time off and parental leave
• Social and team building events monthly, quarterly, and annually
• Monthly internet reimbursement