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Senior Machine Learning Engineer – ML Platform & Operations
Hatchyardlinkedin.com
Nov 9, 2026, 12:41 PM1d ago
Full-timeNugegoda, Western ProvinceML & AISenior (5+ yrs)
About the role
The Senior Machine Learning Engineer – ML Platform & Operations role involves building, deploying, and maintaining scalable machine learning systems in production. The position focuses on the full ML lifecycle, including model deployment, pipeline automation, performance monitoring, and ensuring operational reliability. The engineer will collaborate with data scientists and software engineering teams to develop robust ML infrastructure, automate workflows, and troubleshoot production issues. Key requirements include significant experience in ML engineering, hands-on production deployment, strong JavaScript/TypeScript skills, and familiarity with cloud platforms and monitoring tools.
Must have
- 5+ years of experience in machine learning, ML infrastructure, cloud engineering, platform engineering, SRE, or a related technical field.
- Hands-on experience deploying and operating machine learning models in production environments.
- Strong understanding of ML workflows, including model training, evaluation, inference, deployment, and versioning.
- Practical experience with at least one major cloud platform, such as Microsoft Azure or AWS.
- Strong JavaScript/TypeScript development experience is required. Knowledge of Python or other programming languages is an advantage.
- Familiarity with cloud ML platforms such as Azure Machine Learning, Amazon SageMaker, or equivalent solutions.
- Experience with monitoring and observability tools such as Azure Monitor, Amazon CloudWatch, or Datadog.
- Understanding of logging, metrics, distributed tracing, monitoring, and alerting.
- Experience working with containers, APIs, and automated deployment pipelines.
- Knowledge of Infrastructure as Code (IaC) tools such as Terraform or cloud-native alternatives.
- Understanding of CI/CD, cloud deployments, infrastructure automation, and DevOps practices.
- Familiarity with LLM-based applications and their evaluation, deployment, and operational requirements is an advantage.
- Bachelor's degree in Computer Science, Engineering, Data Science, or a related discipline.
Technologies
- A/B Testing
- AWS
- Azure
- CI/CD
- Cloud Native
- CloudWatch
- Data Science
- Datadog
- DevOps
- Infrastructure as Code
- JavaScript
- LLM
- Machine Learning
- Model Deployment
- Observability
- Python
- SageMaker
- Terraform
- TypeScript
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