MLOps Engineer
CUBE· Technology
BangaloreFull-timePosted 1mo ago
About the role
We’re seeking an MLOps Engineer to own the end-to-end lifecycle of machine learning models in production at CUBE, building infrastructure and operational discipline to keep systems reliable, observable, and cost-effective. The role sits at the intersection of data engineering, software engineering, and machine learning, with scope to establish MLOps as a discipline from the ground up during a period of platform consolidation.
Responsibilities
- Build and maintain ML pipelines — design, automate, and operationalise end-to-end pipelines for model training, evaluation, and deployment using Azure-native tooling including Azure AI Foundry and Azure Machine Learning
- Own model deployment and serving infrastructure — manage containerised model endpoints, versioning, traffic management, and rollback mechanisms across environments
- Implement monitoring and observability — establish model performance monitoring, data drift detection, and alerting frameworks that give the team early warning of degradation in production
- Govern LLM usage across CUBE's platform — take ownership of LLM provider relationships (OpenAI, Azure OpenAI, Anthropic), manage API access and versioning, track token consumption, and optimise cost across workloads
- Manage the LLM gateway and prompt versioning — maintain tooling such as LangSmith or Helicone for tracing, evaluation, and prompt lifecycle management in production environments
- Support experiment tracking and model registry — ensure that experiments are reproducible, models are catalogued, and the path from experiment to production is governed and auditable
- Collaborate with data scientists and engineers — work closely with the Lead Data Scientist and Data Engineering team to understand modelling requirements and translate them into reliable production systems
- Champion engineering best practices — bring CI/CD, infrastructure as code, and automated testing discipline to ML workflows; reduce toil and manual intervention wherever possible
- Drive platform reliability and cost efficiency — monitor infrastructure spend, identify optimisation opportunities, and ensure platform SLAs are met
Qualifications
- 3-4 years of experience into Machine learning, Azure, deployment, pipeline is needed
Skills
Required:Machine learningAzureMLOps
Nice to have:LLM governance
Benefits
- CUBE is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.
Last updated Sep 2, 2026. Interested? Apply on the company site.
About CUBE
Transforming Regulatory Compliance | Reduce Risk & Increase Efficiency with our Compliance Automation Solutions
CUBE offers automated regulatory intelligence solutions & services for global compliance, helping enterprises & midmarket businesses manage regulatory change.
regulatorycomplianceAIregtechregulatory change management
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