Sr. Machine Learning Engineer

Sr. Machine Learning Engineer

Cosa facciamo

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Role riporta a:

Digital Solutions Manager

Ubicazione:

Asia Pacific, India, Karnataka, Bangalore

Luogo di lavoro:

Ibrido

Cosa farai

Sr. ML Ops Engineer

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.

 

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