ML Engineer

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. same as above · Bachelor’s or master’s degree in computer science, Engineering, or a related field.