ML Engineer

Title- ML Engineer ( PV W2 )
Duration - 12+ Months
Location - Houston, TX - Hybrid ( Local )
Visa - USC&GC, GC-EAD, H4-EAD, OPT-EAD Only.
MOI - Skype.

Experience Requirements

5+ years of experience in software engineering, AI engineering, or machine learning engineering roles
Proven experience delivering production AI or ML solutions in a cloud environment
Experience collaborating with cross functional teams across data science, engineering, and architecture
Familiarity with data engineering tools such as Databricks, Spark, Azure Data Factory (Important)
Required Technical Skills

Strong experience with Microsoft Azure, including AI/ML services and cloud native architectures
Hands on experience deploying and operating ML pipelines using Azure Machine Learning
Proficiency in Python and modern software engineering practices
Experience with automation and configuration management, including Ansible
Solid understanding of MLOps, model lifecycle management, and CI/CD for AI systems
Experience with containerization and orchestration technologies (e.g., Docker, Kubernetes)
Working knowledge of security, identity, and access control in enterprise cloud environments
Experience with Microsoft Foundry
Experience implementing or operating agentic AI systems
Familiarity with data engineering tools such as Databricks, Spark, Azure Data Factory
Experience integrating AI services (e.g., cognitive services, computer vision, unstructured data processing)
As an AI Engineer, you will:

Partner with business and technical stakeholders to identify and implement agentic AI and machine learning solutions that improve decision making, workflows, and automation
Design and implement cloud native AI architectures using Microsoft Azure services and established AI design patterns
Collaborate with Data Scientists and other AI Engineers to transform prototypes into production ready, scalable solutions
Build, deploy, and operate enterprise scale machine learning pipelines, emphasizing reliability, performance, and security
Orchestrate and configure infrastructure that enables low latency, resilient AI workloads, leveraging infrastructure as code and automation
Contribute to reusable accelerators, templates, and patterns that improve delivery speed and consistency across teams
Support CI/CD, monitoring, and operational practices for AI and ML systems in production environments
Ways of Working

Ability to work independently as a contractor while integrating effectively with existing teams
Strong communication skills, with the ability to explain complex technical concepts clearly
Results oriented mindset with a focus on delivering business value quickly and reliably