Machine Learning Engineer – AI/ML

Title: Machine Learning Engineer Location: Gurgaon, Haryana (Onsite/Hybrid) Experience: • 4-6 years of hands-on experience in Machine Learning Engineering, Applied Machine Learning, or related roles. • Experience with Pandas, NumPy, Scikit-learn, and related Python libraries. • Hands-on experience with Large Language Models (LLMs). • Strong proficiency in Python.

: We are seeking a highly motivated Machine Learning Engineer with 4–6 years of experience to design, build, deploy, and optimize scalable machine learning solutions that solve real-world business problems. The ideal candidate has hands-on experience in developing production-grade ML models, implementing MLOps best practices, and collaborating with cross-functional teams to deliver AI-driven products.

: Machine Learning Development: • Design, develop, train, and optimize Machine Learning and Deep Learning models for classification, regression, forecasting, NLP, and computer vision applications. • Perform feature engineering, model selection, hyperparameter tuning, and performance evaluation. • Conduct experiments and improve model accuracy, scalability, and reliability. Model Deployment & MLOps: • Deploy, monitor, and maintain ML models in production environments. • Build and manage end-to-end ML pipelines using MLOps best practices. • Implement CI/CD workflows for machine learning applications. • Containerize applications using Docker and orchestrate deployments with Kubernetes. Data Engineering & Processing: • Work with structured and unstructured datasets to build scalable data pipelines. • Process and analyze large datasets using SQL and distributed data processing tools. • Collaborate with data engineering teams to ensure high-quality data availability. Cross-functional Collaboration: • Partner with Data Scientists, Product Managers, Backend Engineers, and Business stakeholders to understand

and deliver ML-powered solutions. • Translate business challenges into scalable machine learning applications. Model Monitoring & Optimization: • Monitor model performance, latency, drift, and reliability in production. • Continuously improve deployed models through retraining and optimization. • Implement logging, monitoring, and alerting mechanisms for ML systems. Research & Innovation: • Stay updated with the latest advancements in Machine Learning, Deep Learning, Generative AI, and MLOps. • Evaluate and integrate new frameworks, tools, and best practices into existing workflows.