Automated Model Engineer

1 day ago

Chennai, Tamil Nadu, India beBeeMachineLearning Full time ₹ 15,00,000 - ₹ 30,00,000
Automation Engineer Job Description

Sutherland is a global provider of business process and technology management services offering an integrated portfolio of analytics-driven back-office and customer-facing solutions that support the entire customer lifecycle.

We have been helping customers across industries from financial services to healthcare, achieve greater agility through transformed and automated customer experiences for over 30+ years.

About the Role:

This role will focus on MLOps practices including model versioning, CI/CD, observability, and performance optimization in cloud and hybrid environments.

Responsibilities:

  • Build and manage CI/CD pipelines for ML models using platforms like MLflow, Kubeflow, or SageMaker.
  • Monitor model performance and health using observability tools and dashboards.
  • Ensure automated retraining, version control, rollback strategies, and audit logging for production models.
  • Support deployment of LLMs, RAG pipelines, and agentic AI systems in scalable, containerized environments.
  • Collaborate with AI Developers and Architects to ensure reliable and secure integration of models into enterprise systems.
  • Troubleshoot runtime issues, latency, and accuracy drift in model predictions and APIs.
  • Contribute to infrastructure automation using Terraform, Docker, Kubernetes, or similar technologies.

Qualifications:

  • Bachelor's degree in computer science, IT, or a related field.

Required Skills:

  • 3–5 years of experience in DevOps, MLOps, or platform engineering roles with exposure to AI/ML workflows.
  • Hands-on experience with deployment tools like Jenkins, Argo, GitHub Actions, or Azure DevOps.
  • Strong scripting skills (Python, Bash) and familiarity with cloud environments (AWS, Azure, GCP).
  • Understanding of containerization, service orchestration, and monitoring tools (Prometheus, Grafana, ELK).

Preferred Skills:

  • Experience supporting GenAI or LLM applications in production.
  • Familiarity with vector databases, model registries, and feature stores.
  • Exposure to security and compliance standards in model lifecycle management.

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