Job Overview

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

1. 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.

2. 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.

3. 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.

4. 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: Bachelor’s 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.

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

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

o 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.