Location: Houston, TX (4 days of the week Monday - Thursday) and work from home on Friday
Duration - 6+ months
Interview Process: 2 virtual rounds
Rate: not to exceed $55 – 60 C2C rate.
Key Responsibilities
Partner with business and technical stakeholders to identify opportunities and deliver AI/ML solutions that drive automation, efficiency, and improved decision-making.
Design and implement cloud-native AI architectures leveraging Microsoft Azure services and established design patterns.
Collaborate with Data Scientists and AI Engineers to transition prototypes into scalable, production-ready solutions.
Build, deploy, and manage enterprise-scale machine learning pipelines, ensuring high performance, reliability, and security.
Develop and orchestrate infrastructure for low-latency, resilient AI workloads using infrastructure-as-code and automation practices.
Contribute to reusable frameworks, accelerators, and templates to enhance delivery consistency and speed across teams.
Implement and support CI/CD pipelines, monitoring, and operational best practices for AI/ML systems in production environments.
Required Technical Skills
Strong experience with Microsoft Azure, including AI/ML services and cloud-native architecture.
Hands-on experience with Azure Machine Learning for deploying and managing ML pipelines.
Proficiency in Python and modern software engineering best practices.
Experience with automation and configuration management tools (e.g., Ansible).
Solid understanding of MLOps, model lifecycle management, and CI/CD for AI systems.
Experience with containerization and orchestration technologies (e.g., Docker, Kubernetes).
Knowledge of enterprise cloud security, identity, and access management practices.
Preferred Qualifications
Experience with Microsoft Foundry.
Hands-on experience with agentic AI systems or intelligent automation frameworks.
Familiarity with data engineering tools such as Databricks, Apache Spark, and Azure Data Factory.
Experience integrating AI services, including cognitive services, computer vision, and unstructured data processing.
Experience Requirements
5+ years of experience in AI engineering, machine learning engineering, or software engineering roles.
Proven track record of delivering production-grade AI/ML solutions in cloud environments.
Experience collaborating across cross-functional teams, including data science, engineering, and architecture.
Ways of Working
Ability to work independently while effectively collaborating within distributed teams.
Strong communication skills with the ability to translate complex technical concepts into clear, business-friendly language.
Results-driven mindset with a focus on delivering scalable solutions and measurable business value.