We are looking for a Senior Machine Learning Engineer to join our Platform Machine Learning team in Bangalore. In this role, you will design and build production-grade machine learning systems that power intelligent capabilities across the SolarWinds observability platform.

This position focuses on building reliable ML systems and LLM-powered services, including model pipelines, scalable inference systems, and modern MLOps workflows. You will work closely with platform engineers, product managers, and other ML engineers to deliver ML-driven features that operate reliably in real-world production environments.

Responsibilities:

• Design, build, and deploy machine learning systems that process large-scale telemetry and operational data.
• Develop and maintain end-to-end ML pipelines, including data preparation, feature engineering, model training, evaluation, and production deployment.
• Build and integrate LLM-powered capabilities, such as Retrieval-Augmented Generation (RAG) pipelines and intelligent automation features.
• Implement and maintain MLOps best practices, including experiment tracking, model monitoring, reproducibility, and automated training and deployment workflows.
• Collaborate closely with platform, infrastructure, and product teams to integrate ML capabilities into distributed SaaS systems.
• Contribute to improving platform reliability and automation through ML-driven insights and predictive systems.
• Evaluate and integrate modern ML tools and techniques that can enhance observability and platform intelligence.
• Support a culture of strong engineering practices including code quality, documentation, and collaborative development.

Must Have

• 5+ years of experience in machine learning engineering or software engineering.
• Strong programming experience in Python.
• Hands-on experience with machine learning frameworks such as PyTorch, TensorFlow, or scikit-learn.
• Experience building and deploying machine learning models in production environments.
• Experience working with cloud platforms such as AWS, Azure, or GCP.
• Experience with MLOps tools or workflows (for example MLflow, Kubeflow, SageMaker, Airflow, or similar tools).
• Experience working with data pipelines or large-scale data processing systems.
• Exposure to LLM frameworks or modern AI tooling such as Hugging Face, LangChain, or OpenAI APIs.
• Experience working with containerized environments such as Docker and Kubernetes.
• Strong problem-solving skills and the ability to collaborate across engineering teams.