Machine Learning Operation

Job Description MLOps Engineer/Lead/Architect

Experience: 7 Years to 16 Years

Location -Bellandur bangalore/Chennai

Shift-1.30pm- 10.30pm

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

Key Skills – MLOps, CI/CD, GIT hub, Python, Ansible, Azure DevOps.

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

• 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

MLOps & ML Platform Engineering

• Design, implement, and maintain end to end MLOps frameworks covering:

o Model training

o Experiment tracking

o Versioning and reproducibility

o Deployment and monitoring

o 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

Cloud Native & Containerized ML Systems

• Develop cloud first solutions using Microsoft Azure, including:

o Azure Machine Learning

o Azure Fabric (as applicable)

• Hands on experience building and deploying ML pipelines using Azure Machine Learning SDK
• Design and manage containerized ML workloads using:

o Docker

o Kubernetes

• 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:

o Linear algebra

o Probability

o Statistics

• Strong understanding of data engineering tools and patterns, including:

o Databricks

o Apache Spark

o Azure Data Factory

Collaboration & Communication

• Ability to engage effectively with technical experts and stakeholders at all organizational levels
• Strong capability to identify opportunities where ML and analytics can:

o Improve business workflows Enable data driven decision making

• Excellent written and verbal communication skills, with the ability to convey complex technical concepts clearly and concisely

Mandatory Skills : MLOps, CI/CD, GIT hub, Python, Ansible, Azure DevOps.