Senior AI Platform Engineer

Technology Engineer This role leads large-scale, complex technology initiatives and helps establish enterprise-wide engineering standards and best practices. It involves full-stack software engineeringdesigning, coding, testing, debugging, and documenting solutionswhile also reviewing and evaluating advanced technical architectures that support both tactical and strategic business goals. A key focus of the role is contributing to Generative AI platform development, including delivering AI/ML models to on-prem and cloud platforms such as GCP Vertex AI and Azure ML. The position participates in daily Agile standups, provides SME guidance to data science teams, and collaborates closely with engineering, platform, and strategy teams to define infrastructure requirements and support cloud migration. The engineer researches industry trends, evaluates new technologies, and drives the adoption of automation-first practices. As an expert resource within DTI, the role supports other technical teams and helps solve complex technical challenges across the organization. The ideal candidate brings strong experience in Linux, DevOps, CI/CD pipelines, infrastructure as code (Terraform), containerization (Docker, OpenShift), cloud platforms (GCP, Azure), AI/ML operations, and cloud-native toolchains. Additional strengths include hands-on Generative AI/LLM capabilities, vector databases, observability stacks, and leading technically complex discussions. Strong analytical, problem-solving, and communication skills are essential. Due to client requirements, applicants must be willing and able to work on a W2 basis. For our W2 consultants, we offer a great benefits package that includes Medical, Dental, and Vision benefits, 401k with company matching, and life insurance. Rate: $70.00 to $75.00/hr. W2 Responsibilities: Lead complex technology initiatives with enterprise-scale impact. Develop and promote standards and best practices for large-scale engineering solutions. Design, code, test, debug, and document software for projects and programs. Review and analyze complex technology architectures to meet tactical and strategic objectives. Contribute to Generative AI platform capabilities and toolchains. Deliver AI models to on-prem infrastructure and cloud platforms including GCP Vertex AI and Azure ML. Participate in daily standups to advance platform capability builds. Provide subject matter expertise guidance to data science teams. Collaborate with engineering strategy, platform engineering, and development teams to define infrastructure requirements and drive cloud migration. Research and evaluate new technologies, and recommend automation-focused solutions. Serve as an expert resource for other technical teams within the organization. Experience Requirements: 4+ years of software engineering experience. 4+ years with Gradle or Maven. 3+ years with Python. 4+ years of build-deploy automation and configuration in Linux and Unix environments. 4+ years troubleshooting across applications and infrastructure. 3+ years with Docker and container image development. 3+ years with OpenShift Container Platform. 3+ years administering Jenkins and creating jobs, and experience with GitHub Actions. 3+ years applying DevOps practices in enterprise CI/CD environments. 3+ years with Terraform including module engineering, state management, and enterprise execution. 3+ years of advanced Infrastructure as Code experience. 3+ years with Google Cloud Platform including managing GCP resources via Terraform. 3+ years with cloud-native AI toolchains and observability stacks including Vertex AI and Azure ML. 3+ years of advanced git/GitHub proficiency. 2+ years with LLMs and Generative AI for developer and operations capabilities. 2+ years with Elasticsearch, vector databases, and model deployment. 2+ years supporting AI/ML solutions in enterprise environments on GCP or Azure. Experience deploying applications to OpenShift and managing OCP workloads (preferred). Understanding of UrbanCode Deploy or Harness CD tools (preferred). GCP Associate Cloud Engineer or higher certification (preferred). Knowledge of cloud computing, PaaS, microservices, and containers (preferred). Experience with Kubernetes and Docker across on-prem and public cloud (preferred). Experience leading analysis of current systems and resolving complex problems (preferred). Ability to lead technically complex discussions and working sessions (preferred). Understanding of MDLC components including model registry, versioning, monitoring, and reproducibility (preferred). Experience with real-time inferencing, event-driven architectures, or high-throughput pipelines (preferred). Strong analytical, problem solving, and communication skills (preferred). Education Requirements: Google Cloud Associate Cloud Engineer certification (preferred).

Created: 2026-03-06