Job
Description
Job Summary: As a Senior Machine Learning Engineer at Traceable, you will be instrumental in transforming ML models from prototype to production at scale. You will work closely with data scientists, MLOps engineers, and product teams to design, develop and deploy critical, high-performing ML solutions. This role requires a blend of engineering, MLOps, and data science skills to streamline model deployment and ensure continuous, reliable operations in the production environments. 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 enablement pipelines for continuous integration and deployment of ML models, ensuring smooth transitions from development to production. Scalability and Optimization: Implement distributed systems and leverage cloud-based architectures (e.g., AWS, GCP) to scale ML models and 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 that can improve model deployment speed and robustness and keep Traceable.ai at the cutting edge of ML infrastructure. Documentation and Knowledge Sharing: Document processes, maintain well-structured codebases, promote best practices in ML engineering, and lead internal knowledge- sharing sessions to foster a culture of continuous improvement and technical excellence. Requirements : 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 experience in deploying ML models in production. Technical Skills: Programming: Proficiency in Python and familiarity with ML libraries (e.g., TensorFlow, PyTorch, Scikit-Learn). MLOps Tools: Experience with CI/CD for ML, containerization (Docker, Kubernetes), and workflow orchestration tools (e.g., Airflow, MLflow). Cloud Infrastructure: Strong knowledge of cloud platforms (AWS or GCP), including managed ML services (SageMaker, Vertex AI). Data Processing: Familiarity with distributed computing frameworks (e.g., Spark, Dask) and data pipelines. Experience with relational databases like MySQL, PostgreSQL and experience with SQL query tuning, performance optimizations is a plus. Problem-Solving: Proven ability to troubleshoot and optimize ML systems in production. Collaboration: Excellent communication and teamwork skills, with experience working in. Adaptability: Ability to thrive in a fast-paced, evolving environment and rapidly adopt new tools and technologies. Work Location Bangalore. The successful candidate will be expected to be in the Bangalore office 3x/ week. What You Will Have at Harness Experience building a transformative product End-to-end ownership of your projects Competitive salary Comprehensive healthcare benefit Flexible work schedule Quarterly Harness TGIF-Off / 4 days Paid Time Off and Parental Leave Monthly, quarterly, and annual social and team building events Monthly internet reimbursement . You can also find additional information about this type of scam and report any fraudulent employment offers via the Federal Trade Commission s website ( https: / / consumer.ftc.gov / articles / job-scams) , or you can contact your local law enforcement agency.
