Job Titles: AI Engineer/Software Engineer
Client Work Locations: Atlanta, Bay Area, Charlotte, Chicago, Cincinnati, Dallas-Metro, Denver, Milwaukee, MSP, NY, Portland, St. Louis (You can choose any Location) - No relocations accepted. 3-5 days Hybrid. Candidates must be local or drivable distance.
36 Months+ Contract (Directly on Client W2)
Pay rate: $65/hr. to $78/hr. W2 (Based off the years of experience)
They need strong AI/GenAI Engineers that can solve the business side of AI (They don’t need Machine Learning Engineers/Data Scientists/Python Developers). AWS/Azure environment but focus is more on AWS experience. It’s the Infrastructure responsibility that they need to have.
Key Skills: RAG architecture, Agentic AI, Lang Graph, Lang Chain, AWS, EKS, AKS and Terraform
We are looking for Al/ML Platform Engineers with a strong software and cloud engineering background, not traditional data scientists focused on training models, building algorithms, or performing statistical analysis. The ideal candidate should have hands-on experience designing, building, and operating enterprise-scale Al platforms on Azure or AWS. We need engineers who can create Infrastructure as Code using Terraform, Bicep, or CloudFormation, build CI/CD pipelines from the ground up, design cloud architectures, and automate platform operations rather than simply consuming existing tools and processes.
Candidates should possess deep expertise in Kubernetes (more on infrastructure side rather development), including cluster architecture, networking, security, upgrades, scaling, observability, and platform reliability. We are looking for engineers who understand how enterprise networking works and can design and implement or work with landing zones, DNS, load balancing, ingress/egress patterns, private networking, connectivity, and security controls, rather than simply deploying applications into an existing environment.
Experience enabling Al and LLM workloads in production is highly desirable. This role is focused on building the platforms that powers Al applications, including model hosting, inferencing, API gateways, observability, security guardrails, access management, cost controls, and operational excellence at scale. We are less interested in candidates whose primary experience centers around developing, fine-tuning, or researching machine learning models and more interested in engineers who can provide the cloud, platform, and operational foundations that allow Al workloads to run securely and reliably in production.
The ideal candidate thinks like a platform engineer, understands distributed systems at high level, cloud-native architecture, DevSecOps practices, automation, reliability engineering, and enterprise-scale operations, and can build reusable self-service platforms that enable hundreds of developers to securely consume Al capabilities across the organization.
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