AI/ML Operations Architect

Hybrid: Mon-Thurs onsite in SA

Duration: 6-month contract with potential extensions

Must Haves:
• Bachelor's degree in Computer Science, Engineering, or a related field
• 4+ years of Azure DevOps, AWS DevOps, Azure ML, or Linux-based platform engineering experience
• 3+ additional years of experience designing and overseeing Machine Learning operations (MLOps) as an architect
• Proficient in Azure Cloud Platform or AWS Cloud Platform
• Docker and Kubernetes containerization experience
• Expert programming skills in Python, Bash, PowerShell
• Experience with hands-on deployment of infrastructure as code (Terraform, ARM)
• Experience working with Data Science and Machine Learning teams

Plusses:
• Advanced Analytics Tools
• Experience in Agile methodologies
• Strong communication skills for technical and non-technical stakeholders
• Collaborative mindset for diverse team environments
• Multiple Azure Certifications in Data, AI, ML, or DevOps, Terraform Certification, CKA Certification
• Microsoft Certified: Azure Data Scientist Associate, AWS Certified Machine Learning -- Specialty, or Google Certified Professional Machine Learning Engineer

D2D:

Insight Global is looking for an AI/ML Operations Architect to sit onsite in San Antonio, TX 4 days a week and remote 1 day a week. The candidate will collaborate with a team of MLOps Architects and Engineers to support the Data Science & AI Team. In this role, the candidate will be designing and overseeing the implementation of Artificial Intelligence (AI), Machine Learning Operations (MLOps), and Lifecycle of Large Language Model Operations (LLMOps) solutions within the Azure AI/ML platform. They will work with cross-functional teams to optimize the AI/ML Azure stack at the client, ensuring efficient and high-quality delivery of AI/ML initiatives.

My client is leading an MLOps team which is working with Data Scientists and taking their developed models to productionize them and lot of it may require building out infrastructure for each case. This team will be working behind the scenes. Most of tools are in Azure space such as OpenAI, AzureML, Computer Vision, AI Search, Cognitive Services, Graph Database, and possibly Vector Database. MLOps is more on Monitoring and Operations side of the Data Science house where we need to productionize, monitor, retrain, deploy, etc.