Machine Learning Ops Engineer Full-time

Roles and Responsibilities:
• Design and implement scalable ML pipelines in cloud and on-premise environments.
• Automate model deployment and monitoring processes, including data drift and model drift
detection using tools like Evidently AI and Alibi Detect.
• Develop and optimize CI/CD pipelines tailored for ML workflows, integrating DevOps
principles.
• Ensure model governance, reproducibility, and compliance, with an emphasis on
Responsible AI.
• Collaborate closely with Data Engineers, Data Scientists, and DevOps teams to integrate
ML models into production systems.
• Perform regular audits and tuning of deployed models.
• Implement model explainability techniques using tools like SHAP, LIME, or Azure Machine
Learning’s interpretability features.
• Troubleshoot complex issues in ML pipelines and infrastructure.
• Mentor junior engineers and provide technical guidance.
• Lead the creation of YAML-based pipelines for complex ML workflows.
• Utilize cloud-specific ML platforms such as Azure Machine Learning, AWS Sagemaker, and
Google AI Platform.
• Implement and manage cloud-specific container services like Azure Kubernetes Service
(AKS) and Amazon EKS.
• Apply Infrastructure-as-Code (IaC) practices to manage ML infrastructure using Terraform,
CloudFormation, or ARM templates.
• Work closely with DevOps teams to align ML operations with broader IT operations and
deployment strategies.

#LI-PH

Required Skills:
• Strong understanding of Machine Learning and data processing pipelines.
• Proficiency in cloud platforms (AWS, Azure, GCP) and associated ML services.
• Advanced knowledge of CI/CD tools and DevOps practices.
• Experience with Docker, Kubernetes, and other container orchestration tools.
• Proficiency in scripting languages (Python, Bash) and automation tools.
• Familiarity with ML frameworks (TensorFlow, PyTorch, Scikit-learn).
• Experience with YAML-based pipeline creation.
• Knowledge of data drift and model drift monitoring tools (e.g., Evidently AI, Alibi Detect).
• Strong knowledge of version control, model tracking (e.g., MLflow), and experiment
management.
• Understanding of Responsible AI principles.
• Experience with model explainability tools and techniques (e.g., SHAP, LIME).
• Proficiency in Infrastructure-as-Code (IaC) tools like Terraform, CloudFormation, or ARM
templates.
• Excellent problem-solving and communication skills.
Minimum 3 year(s) of experience is required

About Accenture

Accenture is a leading global professional services company that helps the world’s leading businesses, governments and other organizations build their digital core, optimize their operations, accelerate revenue growth and enhance citizen services-creating tangible value at speed and scale. We are a talent- and innovation-led company with 750,000 people serving clients in more than 120 countries. Technology is at the core of change today, and we are one of the world’s leaders in helping drive that change, with strong ecosystem relationships. We combine our strength in technology and leadership in cloud, data and AI with unmatched industry experience, functional expertise and global delivery capability. We are uniquely able to deliver tangible outcomes because of our broad range of services, solutions and assets across Strategy & Consulting, Technology, Operations, Industry X and Song. These capabilities, together with our culture of shared success and commitment to creating 360° value, enable us to help our clients reinvent and build trusted, lasting relationships. We measure our success by the 360° value we create for our clients, each other, our shareholders, partners and communities. Visit us at www.accenture.com

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