- Design, develop, and deploy scalable ML models and AI solutions for real-world business problems.

- Implement robust MLOps pipelines, including model versioning, CI/CD, monitoring, drift detection, and automated retraining.

- Work with large datasets to build, train, evaluate, and optimize predictive models.

- Collaborate with data engineers and cross-functional teams to deploy ML systems into production.

- Perform data preprocessing, feature engineering, and exploratory data analysis (EDA).

- Develop reusable ML components, workflows, and automation processes.

- Diagnose issues in data, model performance, production failures, and pipeline bottlenecks.

- Ensure models meet business, performance, and scalability requirements.

- Maintain clear documentation for models, pipelines, and experiments.

Required Skills & Experience :

- 3+ years of hands-on experience as an ML Engineer, AI Engineer, or Data Scientist.

- Strong programming skills in Python and experience with ML frameworks (TensorFlow, PyTorch, Scikit-learn).

- Mandatory experience with MLOps tools and practices (MLflow, Kubeflow, SageMaker, DVC, CI/CD, model monitoring).

- Solid understanding of predictive modelling, statistics, and algorithm selection.

- Experience working with SQL for data extraction, transformation, and analysis.

- Strong skills in creating ML pipelines and deploying models in production.

- Hands-on experience with cloud platforms (AWS/Azure/GCP) is preferred.

- Excellent analytical, problem-solving, and communication skills