ML Engineer

Job description

Job Description: Key Responsibilities - Design, develop, and deploy machine learning models for AI-driven business solutions. - Build and maintain scalable ML pipelines covering data ingestion, feature engineering, model training, validation, deployment, and monitoring. - Implement MLOps best practices including experiment tracking, model versioning, CI/CD, model governance, and automated retraining. - Collaborate with Data Scientists and Data Engineers to operationalize machine learning solutions and accelerate model deployment. - Develop and optimize distributed data processing workflows using Spark/PySpark and cloud-native technologies. - Monitor model performance, data drift, and infrastructure health, ensuring reliability and scalability in production. - Build Endpoints and inference services for real-time and batch scoring applications. - Implement automated testing, validation, and deployment pipelines for ML workloads. - Develop and deploy GenAI applications leveraging LLMs, RAG frameworks, vector databases, and prompt engineering. - Work closely with DevOps teams to optimize cloud infrastructure, security, scalability, and deployment processes. - Maintain technical documentation, architectural designs, and operational runbooks. Required Qualifications - 5+ years of experience in Machine Learning Engineering, Data Science, MLOps, or Data Engineering. - Experience with MLOps platforms such as MLflow, Azure ML, Databricks - Strong knowledge of CI/CD pipelines, Git/GitHub, containerization (Docker), and orchestration platforms (Kubernetes). - Exposure in deploying a use case in production leveraging Generative AI involving prompt engineering and RAG Framework - Experience with Spark/PySpark and distributed data processing frameworks. - Hands-on experience deploying and managing machine learning models in production environments. - Experience working with Azure, AWS, or GCP cloud ecosystems. - Exposure to Kafka or streaming frameworks for real-time inference and data processing. - Strong proficiency in Python programming language. - Understanding of model monitoring, data drift detection, model explainability, and AI governance. - Strong problem-solving skills and the ability to iterate and experiment to optimize AI model behavior. - Strong analytical, problem-solving, and stakeholder communication skills. Preferred Qualifications - Experience with Generative AI, LLMs, Agentic AI, and RAG-based applications. - Experience with Databricks Lakehouse, MLflow, Unity Catalog, and Delta Lake. - Relevant certifications in Cloud, Machine Learning, Data Engineering, or MLOps. Responsibilities: same as above Qualifications: · Bachelor’s or master’s degree in computer science, Engineering, or a related field.