Scope
• Position is focused on supporting and enhancing AI/ML platforms and MLOps pipelines for enterprise-scale applications.
• Build and manage end-to-end AI lifecycle workflows, including model training, validation, deployment, monitoring, and retraining.
• Work closely with Data Science, DevOps, Infrastructure, Platform Engineering, and Application teams.
• Ensure production AI systems are stable, observable, and compliant with governance standards.
• Support AI platform operations during steady state and drive continuous improvements.
• Enable reusable and standardized AI platform components for multiple use cases across the organization.
• Contribute to improving reliability, scalability, and performance of AI-driven systems.
Our Current Technical Environment
• Cloud Architecture: MS Azure
• AI/ML Platforms: Azure ML / OpenAI / MLflow (or similar)
• Programming: Python
• Data & Processing: SQL, Data Pipelines, REST APIs
• MLOps / DevOps Tools: CI/CD pipelines, Git, Azure DevOps
• Frameworks / Others: Model Monitoring, Logging, Experiment Tracking, Containerization (Docker), Kubernetes (preferred)
What You’ll Do
• Design, implement, and support AI/ML pipelines for training, testing, deployment, and inference workflows.
• Build and maintain MLOps frameworks for model versioning, experiment tracking, and reproducibility.
• Develop automation for model deployment, rollback, scaling, and lifecycle management.
• Establish model monitoring frameworks for performance, drift detection, data quality, and reliability.
• Collaborate with Data Scientists to operationalize models into production-ready systems.
• Integrate AI workflows into CI/CD pipelines for seamless deployment and updates.
• Troubleshoot production issues related to AI models, pipelines, and infrastructure; perform root cause analysis and implement permanent fixes.
• Ensure AI systems meet performance, scalability, security, and compliance requirements.
• Create reusable components and templates for standardizing AI deployment and operations.
• Implement logging, observability, and alerting mechanisms for AI services.
• Support governance requirements such as model traceability, auditability, and version control.
• Document AI platform processes, standards, and operational procedures.
• Provide guidance and support to cross-functional teams on AI platform usage and best practices.
• Drive continuous improvement in AI platform reliability, automation, and operational efficiency.
What We Are Looking For
• Bachelor’s degree in engineering, Computer Science, Data Science, or a related field.
• Minimum 6 years of overall IT experience, with at least 3+ years in AI/ML platform engineering, MLOps, or AI production support.
• Strong programming skills in Python, with experience in building automation and data/ML workflows.
• Hands-on experience with AI/ML platforms such as Azure ML, MLflow, or similar ecosystems.
• Experience in building and managing end-to-end ML pipelines and model lifecycle workflows.
• Good understanding of model deployment patterns, APIs, batch/real-time inference systems.
• Experience with CI/CD pipelines, version control (Git), and DevOps practices.
• Familiarity with containerization (Docker) and orchestration tools like Kubernetes (preferred).
• Strong understanding of model monitoring, drift detection, logging, and observability practices.
• Experience working with data pipelines, SQL, and data processing frameworks.
• Understanding of AI governance, model explainability, auditability, and compliance considerations.
• Ability to work collaboratively with data scientists, engineers, DevOps, and platform teams.
• Exposure to LLMs, generative AI workflows, or AI-driven automation use cases is an added advantage.
Our Values
If you want to know the heart of a company, take a look at their values. Ours unite us. They are what drive our success – and the success of our customers. Does your heart beat like ours? Find out here: Core Values
All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability or protected veteran status.
