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.

same as above

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