Senior MLOps Engineer
- Sri Lanka
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