ML ops

**Job Description:**

**The ML Ops Lead drives the design, deployment, and optimization of machine learning solutions, balancing hands-on engineering with strategic leadership to enable robust, scalable, and maintainable AI infrastructure.**

**Key Responsibilities:**

* Architect and maintain scalable ML infrastructure, self-service ML pipelines, and CI/CD workflows for model training and deployment.
* Lead and mentor an MLOps team, fostering technical excellence and continual improvement.
* Design high-scale distributed training and inference environments using cloud (AWS, GCP) and on-premises resources.
* Build and manage feature stores, data ingestion, preprocessing, and validation pipelines.
* Implement A/B testing, canary releases, monitoring, and rollback mechanisms for production ML models.
* Ensure compliance with data governance, privacy, and security standards; manage role-based access controls for ML infrastructure.
* Collaborate with data scientists, software engineers, DevOps, and product teams to bring models from experimentation to enterprise-grade production.

**Required Skills and Experience:**

* Deep expertise in creating and managing machine learning infrastructure and orchestration frameworks (e.g., Kubeflow, MLflow, Airflow).
* Proficiency in cloud platforms (AWS, GCP), Kubernetes, Terraform, and distributed computing.
* Having databricks MLflow knowledge.
* Excellent skills in Python and ML frameworks (TensorFlow, TorchServe), CI/CD automation, and pipeline management.
* Strong analytical, problem-solving, and project management abilities.
* Demonstrated ability to build, scale, and lead technical teams.
* Solid understanding of data compliance, governance, and model monitoring.
* Master’s degree in a technical field (Computer Science, Data Science, ML, or equivalent).

**Desired Qualifications:**

* Experience optimizing GPU/TPU utilization and large-scale storage solutions.
* Track record in designing robust monitoring systems for model drift, downtime, and performance.
* Familiarity with the challenges of deploying models in real-time, multi-cloud, or edge environments.
* Ability to innovate and continuously improve workflows, combining ML and human computation.