ML Ops Engineer

Important Information

Location: Chennai

Experience: 6+ years

Job Mode: Full-time

Work Mode: Hybrid 

Job Summary

Design the data pipelines and engineering infrastructure to support our clients’ enterprise machine learning systems at scale

Responsibilities and Duties

• 5+ years of experience in the Machine Learning Operations / Dev Ops role
• Python, Kubeflow, Github Actions, Kubernetes, Docker
• Poetry for package management
• Black, ruff, isort, flake8 for code cleanup
• Design the data pipelines and engineering infrastructure to support our clients’ enterprise machine learning systems at scale
• Take offline models data scientists build and turn them into a real machine learning production system
• Develop and deploy scalable tools and services for our clients to handle machine learning training and inference
• Identify and evaluate new technologies to improve performance, maintainability, and reliability of our clients’ machine learning systems
• Apply software engineering rigor and best practices to machine learning, including CI/CD, automation, etc.
• Support model development, with an emphasis on auditability, versioning, and data security
• Facilitate the development and deployment of proof-of-concept machine learning systems
• Communicate with clients to build requirements and track progress
• Strong analytic skills related to working with structured, semi structured and unstructured datasets

Qualifications and Skills

ML Operations, CI/CD, Python, Kubeflow

About Encora

Encora is the preferred digital engineering and modernization partner of some of the world’s leading enterprises and digital native companies. With over 9,000 experts in 47+ offices and innovation labs worldwide, Encora’s technology practices include Product Engineering & Development, Cloud Services, Quality Engineering, DevSecOps, Data & Analytics, Digital Experience, Cybersecurity, and AI & LLM Engineering. 

At Encora, we hire professionals based solely on their skills and qualifications, and do not discriminate based on age, disability, religion, gender, sexual orientation, socioeconomic status, or nationality.