Walk-in | ML/Machine learning Ops Engineer

Infogrowth; Chennai District; On-site; INR 1,500,000 - 2,500,000; Full time

Job summary

InfoGrowth is seeking an experienced ML Engineer/ML Ops Engineer in Chennai to design and deploy scalable ML pipelines and production systems. You will turn models into robust services, implement CI/CD, and collaborate with clients to capture requirements and ensure security and auditability.

Candidates should have a CS/IT degree and 5+ years of ML Ops experience, with a focus on performance and reliability.

Qualifications

• University Degree in Computer Science, Information Technology, or related field.
• 5+ years of experience in the Machine Learning Operations role.
• Design data pipelines and engineering infrastructure to support enterprise ML systems at scale.
• Translate offline models into production ML systems.
• Develop and deploy scalable tools and services for ML training and inference.
• Evaluate technologies to improve performance, maintainability, and reliability.
• Apply software engineering rigor to ML, including CI/CD and automation.
• Support model development with auditability, versioning, and data security.
• Facilitate the development and deployment of proof-of-concept ML systems.
• Communicate with clients to gather requirements and track progress.
• Strong analytics for structured, semi-structured, and unstructured data.
• Advanced ML techniques: trees, forests, boosting, neural nets, deep learning, SVM, clustering, Bayesian networks, RL, feature reduction.

Responsibilities

• Design and implement data pipelines and ML infra for enterprise scale.
• Turn offline models into robust production ML systems.
• Develop and deploy scalable ML training and inference tools.
• Evaluate new technologies to improve performance and reliability.
• Apply CI/CD and automation to ML workflows and pipelines.
• Assist model development with auditability, versioning, and data security.
• Support PoC ML systems from development to deployment.
• Collaborate with clients to define requirements and track progress.
• Handle data challenges across structured, semi-structured and unstructured data.

Skills

ML Ops

Data pipelines

CI/CD

Automation

Auditability

Versioning

Data security

Proof-of-concept ML

Client communication

Structured data

Neural networks

Deep learning

SVM

Reinforcement learning

Random Forest

Decision trees

Clustering

Feature reduction

Python

Education

Bachelor's degree in CS/IT or related field

Job description

Hello Candidate,

About InfoGrowth (Staffing & Consulting Services): www.infogrowth.in

InfoGrowth is a technology staffing and consulting company providing skilled IT professionals to global clients across implementation, support, and transformation projects. Our focus is on quality talent delivery, faster closures, and long-term client partnerships.

We are currently evaluating your profile for a ML Engineer / ML Ops Engineer opportunity. Your experience looks relevant, and we would like to share the detailed Job Description and request a few details from your side.

ML Engineer / ML Ops Engineer

Chennai

5+ years of experience

Task Description

• University Degree in Computer Science, Information Technology, or related field
• 5+ years of experience in the Machine Learning Operations role
• 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
• Advanced Machine learning techniques: Decision Trees, Random Forest, Boosting Algorithm, Neural Networks, Deep Learning, Support Vector Machines, Clustering, Bayesian Networks, Reinforcement Learning, Feature Reduction.