Senior - Machine Learning Engineer
About the role
The Senior Machine Learning Engineer will be responsible for deploying and operating machine learning models and services in production, supporting ML workflows across the full lifecycle, and building reliable pipelines for model development and release. The role involves monitoring model and service performance, identifying issues such as model degradation or drift, improving model serving systems, and supporting experimentation and A/B testing. The engineer will troubleshoot ML pipelines and collaborate with data science and engineering teams to enhance model quality and operational reliability. The position requires hands-on experience with ML deployment, cloud platforms, and programming, as well as strong problem-solving and communication skills.
Must have
- 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
- 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
- Bachelor’s degree in Computer Science, Engineering, Data Science, or a related field
- 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
Nice to have
- Knowledge of Python or other programming languages is an advantage
- 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
Technologies
- A/B Testing
- AWS
- Azure
- CI/CD
- CloudWatch
- Data Science
- Datadog
- DevOps
- Infrastructure as Code
- JavaScript
- LLM
- Machine Learning
- Model Evaluation
- Python
- SageMaker
- Terraform
- TypeScript
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