ML Engineer

Role Summary We are seeking a highly motivated Data Scientist / ML Engineer with 68 years of experience in designing, developing, deploying, and managing AI/ML solutions at scale. The ideal candidate should possess strong expertise in the complete machine learning lifecycle, from data preparation and model development to production deployment, monitoring, and optimization. Key Responsibilities Develop and deploy Machine Learning, Deep Learning, Time-Series Forecasting, and Generative AI solutions for business-critical use cases. Lead end-to-end AI model development including data acquisition, feature engineering, model training, validation, deployment, and monitoring. Build scalable ML pipelines and production-grade AI applications using MLOps best practices. Collaborate with Product Managers, Business Teams, Data Engineers, and Software Developers to deliver impactful AI solutions. Implement model governance, versioning, performance monitoring, and retraining frameworks. Integrate AI models into enterprise applications through APIs and cloud-native architectures. Drive continuous improvement in model accuracy, reliability, explainability, and operational performance. Required Skills Strong expertise in Python, SQL, Machine Learning, Deep Learning, Time-Series Forecasting, NLP, LLMs, RAG, and Computer Vision. Hands-on experience with Scikit-Learn, TensorFlow, PyTorch, Pandas, NumPy. Experience with MLOps tools such as MLflow, Azure ML, Model Garden, Vertex AI (Gemini Enterprise), SageMaker, Kubeflow, Docker, Kubernetes, and CI/CD pipelines. Knowledge and experience of Azure/ AWS/GCP cloud platforms. Experience deploying and managing ML models in production environments at enterprise scale. Compensation: Up to 2,600,000.00 per year Work Location: In person .