Senior Machine Learning Engineer - ML Platform & Operations
We’re looking for a Senior Machine Learning Engineer to help build, deploy, and operate machine learning systems in production. You’ll work across the ML lifecycle, from preparing data and deploying models to monitoring their performance and improving reliability. The ideal candidate combines practical ML experience with strong cloud and production engineering skills. You’ll partner with data scientists, ML engineers, and software teams to make models easier to deploy, observe, and maintain at scale.
About the job for Senior Machine Learning Engineer - ML Platform & Operations
Key Responsibilities
- Deploy and operate machine learning models and services in production
- Support ML workflows across data preparation, model training, evaluation, deployment, and monitoring
- Build and maintain reliable pipelines for model development and release
- Monitor model and service performance using logs, metrics, traces, and alerts
- Identify issues such as model degradation, data or concept drift, latency, and inference failures
- Improve model serving systems for scalability, availability, security, and cost efficiency
- Support experimentation, model versioning, evaluation, and A/B testing
- Troubleshoot ML pipelines, integrations, APIs, and runtime issues
- Collaborate with data science and engineering teams to improve model quality and operational reliability
- Contribute to incident investigations, root cause analysis, and ongoing improvements to ML operations
Preferred Skills & Experience
- 5+ years of experience in machine learning, ML infrastructure, cloud engineering, platform engineering, SRE, or a related field
- Hands-on experience deploying or operating machine learning models in production
- Understanding of model evaluation, inference, versioning, and the ML lifecycle
- Experience with at least one major cloud platform, such as Microsoft Azure or AWS
- Experience with JavaScript/TypeScript is required; knowledge of Python or other programming languages is an advantage
- Familiarity with cloud ML services such as Azure Machine Learning or Amazon SageMaker, or equivalent tools
- Familiarity with monitoring tools such as Azure Monitor, Amazon CloudWatch, and Datadog
- Understanding of logging, metrics, tracing, monitoring, and alerting
- Experience with containers, APIs, and deployment pipelines
- Knowledge of infrastructure-as-code tools such as Terraform or platform-specific equivalents
- Familiarity with LLM applications and their evaluation or operational requirements is an advantage
- DevOps experience, especially CI/CD, containers, cloud deployments, and infrastructure automation, is an advantage
- Bachelor’s degree in Computer Science, Engineering, Data Science, or a related field
What We’re Looking For
- Strong problem-solving, troubleshooting, and debugging skills
- A practical understanding of what it takes to run ML systems reliably in production
- Interest in improving model performance, deployment workflows, and ML operations
- Clear communication and a collaborative approach
- A focus on reliability, scalability, automation, and continuous improvement
- Ability to work effectively in a fast-paced, evolving technology environment