17 days ago
Hyderābād, IndiaSenior
Responsibilities
- Design and implement robust, scalable ML pipelines on Azure.
- Automate model training, validation, deployment, and retraining workflows.
- Manage and monitor production models, including versioning, drift detection, and retraining triggers.
- Implement CI/CD pipelines tailored to ML workloads.
- Integrate LLMs into applications using frameworks such as LangChain.
- Implement orchestration frameworks for building and managing agentic systems.
- Collaborate with Data Science and Engineering teams to integrate models with applications and APIs.
- Build data ingestion and transformation pipelines using Azure Data Lake, Databricks, or Synapse.
- Apply security, performance, and cost-optimization best practices in Azure environments.
- Troubleshoot deployment and model performance issues.
Requirements
- At least 5 years of experience in MLOps, DevOps, or Data Engineering roles.
- Hands-on experience with Azure services including Azure Machine Learning, Azure DevOps, Azure Kubernetes Service, Azure Data Lake or Blob Storage, and Azure Functions or Logic Apps.
- Proficiency in Python and experience with ML frameworks such as TensorFlow, PyTorch, or Scikit-learn.
- Familiarity with Docker and Kubernetes.
- Experience with ML model monitoring and logging tools.
- Knowledge of ML model versioning and experiment tracking tools such as MLflow or DVC.
- Understanding of software development best practices and agile methodologies.
- Familiarity with Terraform or Bicep for Azure infrastructure provisioning.
- Exposure to responsible AI and governance frameworks on Azure.
- Preferred experience with LLM applications, LangChain, agentic RAG pipelines, FastAPI model endpoints, OpenAI APIs, vector databases, or semantic search frameworks.
- Experience in regulated industries such as healthcare, pharmaceuticals, or life sciences is preferred.