MLOps Lead

Job Summary :

We are seeking an experienced MLOps Engineer to join our team and take ownership of building and managing robust Machine Learning Operations pipelines. The role involves working on cloud platforms, automating ML workflows, monitoring deployed models, ensuring reproducibility, and implementing version control for both code and models. You will collaborate closely with data scientists, ML engineers, and DevOps teams to deliver scalable and reliable ML solutions in production.

Key Responsibilities :

• Design, implement, and maintain end-to-end ML pipelines for training, testing, and deploying machine learning models in production.
• Work with cloud platforms (AWS, GCP, Azure) to provision, manage, and optimize ML infrastructure.
• Implement workflow orchestration using Kubeflow, Airflow, or MLFlow to automate model training, evaluation, and deployment.
• Set up and manage model monitoring systems for performance tracking, drift detection, and health checks using tools like Prometheus, Grafana, Seldon, or Evidently AI.
• Establish and enforce practices for model and code version control using Git and related tools to ensure reproducibility and traceability.
• Collaborate with Data Science teams to productionize models, ensuring efficiency, scalability, and low-latency performance.
• Work with DevOps teams to implement CI / CD pipelines for ML workflows.
• Optimize compute, storage, and cost efficiency of ML workloads in cloud environments.
• Troubleshoot technical issues related to deployment, scaling, and monitoring of ML models.
• Maintain comprehensive documentation for ML pipelines, deployment processes, and monitoring dashboards.

Technical Skills and Qualifications :

Good-to-Have Skills :

Education :

(ref : hirist.tech)