We are seeking an experienced Senior Machine Learning Engineer who can work independently, collaborate effectively with data scientists and engineers, and help accelerate the delivery of enterprise AI solutions from proof of concept through production deployment. This is a highly technical, hands-on role focused on building, deploying, and operationalizing machine learning solutions in cloud environments.
Key Responsibilities
- Build, deploy, and operationalize end-to-end machine learning solutions in cloud environments.
- Develop scalable ML pipelines covering data preparation, feature engineering, model development, deployment, monitoring, and production support.
- Take machine learning models from proof of concept through production deployment.
- Build and maintain scalable ML services and integrate machine learning solutions into enterprise applications and operational workflows.
- Implement and manage MLOps practices, including CI/CD pipelines, model deployment, monitoring, and lifecycle management.
- Collaborate with data scientists and engineers to accelerate enterprise AI initiatives.
- Provide ongoing production support for deployed machine learning models and services.
Required Skills
- 8–10 years of experience in Machine Learning Engineering.
- Solid hands-on experience building, deploying, and operationalizing machine learning solutions.
- Expert-level proficiency in Python and SQL.
- Strong experience with:
- Azure Machine Learning (Azure ML)
- Databricks
- MLflow
- CI/CD Pipelines
- MLOps
- Model Deployment
- Model Monitoring
- Machine Learning Lifecycle Management
- Experience developing scalable ML pipelines and production-ready ML services.
- Experience integrating machine learning solutions into enterprise applications.
- Strong understanding of cloud-native machine learning platforms and automated deployment processes.
- Experience providing production support for ML solutions.
- Ability to work independently while collaborating effectively with cross-functional teams.
Nice to Have
- Experience with Generative AI and Large Language Model (LLM) applications.
- Experience with Agentic AI frameworks.
- Experience with GenAIOps practices, including evaluation, monitoring, deployment, and lifecycle management of GenAI solutions.