ML Engineer

What You'll Do You will join our high-performance Data & AI engineering team and contribute to designing, developing, and deploying scalable Machine Learning solutions that power intelligent enterprise applications and AI-driven business outcomes. Develop, train, evaluate, and deploy Machine Learning models for enterprise and product use cases. Design and implement solutions for regression, classification, clustering, anomaly detection, and predictive analytics. Build reusable feature engineering, data preprocessing, and model training pipelines. Work with large-scale structured and unstructured datasets using Python and SQL. Collaborate with Data Engineers and AI Engineers to productionize Machine Learning models. Build end-to-end ML pipelines covering data ingestion, training, validation, deployment, inference, monitoring, and automated retraining. Deploy Machine Learning models as REST APIs using FastAPI, Flask, or similar frameworks. Optimize models for accuracy, scalability, reliability, and production performance. Monitor deployed models for prediction quality, model drift, and retraining requirements. Containerize ML applications using Docker and support automated deployments through CI/CD pipelines. Participate in architecture discussions, code reviews, technical documentation, and continuous improvement initiatives. Collaborate with Product Managers, Software Engineers, Data Engineers, and business stakeholders to deliver AI-driven solutions. What We Seek In You 3+ years of experience in Machine Learning Engineering, Applied AI, or Data Science. Strong proficiency in Python, SQL, Scikit-learn, TensorFlow or PyTorch, NumPy, and Pandas. Strong understanding of supervised and unsupervised learning, regression, classification, clustering, anomaly detection, feature engineering, model evaluation, and hyperparameter tuning. Hands-on experience building and deploying Machine Learning models using FastAPI or Flask REST APIs. Experience working with Docker, CI/CD pipelines, Git, and cloud platforms such as Azure, AWS, or GCP. Good understanding of scalable ML systems, model optimization, monitoring, model drift detection, and retraining strategies. Strong analytical, debugging, and problem-solving skills. Excellent communication and stakeholder collaboration abilities. Ability to work effectively in agile, cross-functional engineering teams. Preferred Qualifications Experience with MLflow, Kubeflow, Apache Airflow, Azure Machine Learning, or AWS SageMaker. Exposure to NLP, Computer Vision, or Recommendation Systems. Familiarity with Apache Spark or Hadoop. Knowledge of Responsible AI, Model Explainability, AI Fairness, Bias Detection, and AI Governance. Experience with Kubernetes or container orchestration platforms. Domain experience in Manufacturing, Automotive, Supply Chain, Financial Services, Healthcare, or Enterprise Analytics is an added advantage. Life At Next At our core,…