Machine Learning Engineer

Key Responsibilities • Design, develop, and deploy scalable machine learning models into production environments • Architect and implement end-to-end ML pipelines (data ingestion → feature engineering → model training → deployment → monitoring) • Build and maintain robust data preprocessing and feature engineering frameworks • Optimize model performance, scalability, and reliability in cloud environments • Implement CI/CD for ML workflows and automate model retraining processes • Collaborate with software engineers to integrate ML solutions into applications and APIs • Conduct A/B testing and evaluate model performance using statistical techniques • Establish monitoring, logging, and alerting mechanisms for deployed ML models • Mentor junior data scientists and review technical architecture decisions • Translate complex business problems into scalable ML solutions Required Qualifications • 6+ years of experience in Data Science / Machine Learning Engineering • Strong proficiency in Python and ML libraries such as Scikit-learn, TensorFlow, PyTorch • Hands-on experience with ML model deployment and MLOps practices • Experience building and maintaining data pipelines using tools like Airflow, Spark, or similar • Strong understanding of software engineering principles and version control (Git) • Experience with REST APIs, Docker, Kubernetes, or containerized deployments • Solid knowledge of SQL and working with large-scale structured & unstructured datasets • Experience working with cloud platforms such as AWS, GCP, or Azure • Deep understanding of statistics, probability, and experimental design Preferred Qualifications • Experience with LLMs, NLP systems, or Generative AI frameworks • Familiarity with feature stores and model registry tools • Exposure to real-time ML systems and streaming data pipelines • Experience working in Agile/Scrum environments • Strong communication skills with the ability to present technical findings to non-technical stakeholders