: 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
- Solid 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.

📌 ML Engineer (Uttar Pradesh)
🏢 EXL Service
📍 Uttar Pradesh