Machine Learning Operation

Confidential; Bengaluru; Hybrid; INR 1,800,000 - 3,000,000; Full time

Job summary

Confidential seeks an experienced MLOps Engineer/Lead/Architect to deliver and support production-scale ML systems, with hands-on Python and cloud-first ML pipelines. Role includes building end-to-end MLOps frameworks, adapting ML techniques to business problems, and managing deployment and retraining workflows.

The ideal candidate will have strong experience with Azure ML SDK, Docker, Databricks, MLflow, and large-scale distributed training, plus excellent communication skills and collaboration

Qualifications

• 7+ years in ML/AI engineering or MLOps.
• Hands-on Python development experience.
• Experience delivering production ML systems.

Responsibilities

• Deliver and support production-scale ML systems.
• Apply ML techniques to real business problems.
• Integrate ML models in enterprise AI workflows.
• Design end-to-end MLOps frameworks with experiment tracking, versioning, deployment and retraining.

Skills

Python

Machine Learning

MLOps

Distributed training

Cloud computing

Experiment tracking

CI/CD

Big data tooling

Tools

Azure ML SDK

Docker

Databricks

Kubernetes

MLflow

APIs for training/inference

Job description

Job Description MLOps Engineer/Lead/Architect

Experience: 7 Years to 16 Years

Shift-1.30pm- 10.30pm

Work Mode: Monday - WFH and rest 4 Days - work from office

Hands on experience in object oriented and/or functional programming using Python

Key Responsibilities

• Proven experience delivering and supporting production scale ML systems
• Hands on exposure to computer vision and/or cognitive services within production AI solutions
• Ability to assess and apply machine learning techniques to solve real business problems
• Experience integrating ML models on unstructured data as part of enterprise AI workflows
• Design, implement, and maintain end to end MLOps frameworks covering:
  • Experiment tracking
  • Versioning and reproducibility
  • Deployment and monitoring
  • Automated retraining workflows
• Implement and operationalize MLflow or equivalent frameworks for experiment tracking and lifecycle management
• Build custom APIs for ML model training and inference, supporting both batch and real time workloads
• Develop cloud first solutions using Microsoft Azure, including:
  • Hands on experience building and deploying ML pipelines using Azure Machine Learning SDK
• Design and manage containerized ML workloads using:
  • Docker
• Orchestrate large scale ML/DL workloads, including distributed training and massively parallel model execution on cloud infrastructure

Data, Scale & Performance Engineering

• Proficient in orchestrating large scale ML and DL jobs using big data tooling and modern container orchestration platforms
• Experience with model optimization and hyperparameter tuning at scale for ML/DL models
• Working knowledge of mathematics and algorithms, including:
  • Probability
  • Statistics
• Strong understanding of data engineering tools and patterns, including:
  • Databricks
• Ability to engage effectively with technical experts and stakeholders at all organizational levels
• Strong capability to identify opportunities where ML and analytics can
• Excellent written and verbal communication skills, with the ability to convey complex technical concepts clearly and concisely