MLOps Engineer - Azure ML & ML Platform Engineering

Position: Senior MLOps Engineer Azure ML & ML Platform Engineering

Duration: 6 Months.
Location: Bangalore(Client office)
Exp: 7+ Years
Shift Time: 1:30 PM to 11 PM

Job Description MLOps Engineer

Experience 7 Years
Location Hybrid Onsite

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:
Model training
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
Cloud Native & Containerized ML Systems

Develop cloud first solutions using Microsoft Azure, including:
Azure Machine Learning
Azure Fabric (as applicable)
Hands on experience building and deploying ML pipelines using Azure Machine Learning SDK
Design and manage containerized ML workloads using:
Docker
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:
Linear algebra
Probability
Statistics
Strong understanding of data engineering tools and patterns, including:
Databricks
Apache Spark
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:
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