We are seeking a Sr. MLOps Engineer with 5+ years of experience to design, automate, and manage the lifecycle of machine learning models. This role is focused on building high-performance, scalable ML infrastructure on Microsoft Azure that bridges the gap between data science and production-grade engineering. You will be responsible for creating a "Plug-and-Play" deployment framework that ensures our ML solutions are resilient, secure, and cost-optimized.

Key Responsibilities :

Pipeline Architecture & Automation :

- Scalable ML Pipelines : Design and manage end-to-end ML pipelines using Azure ML, Databricks, and PySpark to handle large-scale data processing and model training.

- DevSecOps Integration : Build and maintain automated CI/CD pipelines using GitHub Actions, integrating SonarQube to

enforce strict code quality and security standards.

- Reusable Frameworks : Develop modular templates for various ML use cases to streamline deployment and drive operational efficiency across the enterprise.

Deployment & Orchestration :

- Containerization : Utilize Azure Kubernetes Service (AKS) and Docker to containerize and deploy ML models, ensuring high availability and seamless scaling.

- API Management : Design and manage robust, secure APIs to facilitate seamless interactions between ML models and downstream applications.

- Solution Architecture : Understand and contribute to the overall system architecture to ensure ML components are modular and scalable.

Optimization & Governance :

- Model Lifecycle Management : Perform model optimization, monitor for data drift, and implement automated data refresh checks to maintain model accuracy.

- Cost Engineering : Implement cost-monitoring strategies to ensure efficient resource utilization during high-compute training and deployment phases.

- Documentation : Provide detailed technical documentation for workflows, pipeline templates, and optimization strategies to ensure long-term maintainability.

Collaboration :

- Cross-Functional Synergy : Act as the technical liaison between Data Scientists, DevOps, and IT teams to ensure smooth model transitions across Dev, QA, and Production environments.

Required Qualifications :

- Education : Bachelors degree in engineering, Computer Science, or a related field.

- Experience : 5+ years of total experience with a deep focus on the Azure MLOps tool stack.

- Production Mastery : Proven track record of deploying and maintaining ML models in high-scale production environments.

Technical Proficiency :

- Hands-on expertise with Azure Machine Learning and Databricks.

- Strong understanding of Kubernetes (AKS) or API-based deployment platforms.

- Solid grasp of DevOps practices and containerization (Docker).

- Experience with code quality automation tools like SonarQube.

- Soft Skills : Exceptional problem-solving skills and the ability to thrive in a fast-paced, collaborative environment.

Desired Qualifications :

- Architectural Mindset : Familiarity with broader solution architecture principles is a strong plus.

- Certifications : Azure certifications such as AI-900, DP-100, or AZ-305 are highly preferred.

Job Type : Payroll