We are looking for an experienced MLOps Engineer to build, deploy, and manage scalable machine learning platforms and production AI systems. The ideal candidate should have strong expertise in machine learning lifecycle management, cloud infrastructure, automation, CI/CD, and production deployment of ML models.

Key Responsibilities :

- Design and develop scalable MLOps platforms and data pipelines to support enterprise machine learning workloads.

- Deploy, monitor, and maintain machine learning models in production environments.

- Collaborate with Data Scientists to operationalize ML models and transform research prototypes into production-ready solutions.

- Build and optimize ML training, inference, and model-serving pipelines.

- Develop automation frameworks for model deployment, monitoring, retraining, and lifecycle management.

- Implement CI/CD pipelines for machine learning workflows and infrastructure automation.

- Ensure model versioning, reproducibility, auditability, security, and governance throughout the ML lifecycle.

- Monitor model performance, detect model drift, and support continuous model improvement.

- Evaluate and implement new MLOps tools, frameworks, and cloud technologies to improve scalability and reliability.

- Work with structured, semi-structured, and unstructured datasets to support enterprise AI solutions.

- Collaborate with clients and cross-functional teams to gather requirements, deliver solutions, and provide technical guidance.

- Support proof-of-concept (PoC) development and enterprise AI deployments.

Required Skills :

- 5-8 years of experience in Machine Learning Operations (MLOps), Machine Learning Engineering, or AI Platform Engineering.

- Strong understanding of the complete machine learning lifecycle, including model development, deployment, monitoring, and retraining.

- Experience building data pipelines and scalable ML infrastructure.

- Hands-on experience with Python and machine learning libraries such as TensorFlow, PyTorch, or Scikit-learn.

- Experience with MLOps tools such as MLflow, Kubeflow, SageMaker, Vertex AI, Azure ML, or similar platforms.

- Strong knowledge of Docker, Kubernetes, CI/CD pipelines, and Infrastructure as Code (IaC).

- Experience with cloud platforms such as AWS, Azure, or Google Cloud Platform (GCP).

- Knowledge of model monitoring, versioning, feature stores, data governance, and security best practices.

- Understanding of machine learning algorithms, including Decision Trees, Random Forests, Gradient Boosting, Neural Networks, Deep Learning, Support Vector Machines, Clustering, Bayesian Networks, Reinforcement Learning, and Feature Engineering.

- Strong SQL, data engineering, and analytical skills.

- Excellent problem-solving, communication, and stakeholder management skills.

Education (Mandatory) :

- Full-time B.E./B.Tech, MCA, M.Tech, MS, or M.Sc in Computer Science, Information Technology, Artificial Intelligence, Data Science, or a related field.