AI Model Deployment Administrator
Openkyber
AI Solution Architect for Cincinnati OH Key Responsibilities Brownfield Development: Modernize legacy applications by embedding AI/ML capabilities while maintaining backward compatibility. Cloud Architecture: Design and deploy scalable AI solutions leveraging Azure Cognitive Services, Google Cloud Platform Vertex AI, and containerized microservices. Java Tech Stack: Architect AI modules within Java/Spring Boot applications, ensuring performance and maintainability. Data Engineering: Build and optimize data pipelines using Databricks for AI workloads, integrating structured and unstructured data sources. CI/CD Automation: Implement robust CI/CD pipelines using GitHub Actions and Harness to streamline AI model deployment and application releases. Testing & Validation: Establish automated testing frameworks for AI models, ensuring fairness, robustness, and compliance. Cross-Team Collaboration: Partner with sprint teams to align AI architecture with product roadmaps and delivery timelines. Governance & Compliance: Ensure adherence to ethical AI standards, data privacy regulations, and enterprise governance frameworks. Experience with driving teams through AI/Agentic AI implementation across SDLC phases and AI-first coding. Experience with Agentic AI frameworks like LangChain/LangGraph, MS Agent Framework, CrewAI for custom agent development along with MCP. Proven experience working with business partners & product teams to ideate, conceptualize & scale AI solutions. Exposure to tools like Claude Code, GHCP, MS Fabric, Anthropic, Gemini, and OpenAI LLM models. Required Skills & Experience Proven expertise in AI/ML architecture and cloud-native design. Hands‑on experience with Azure AI services and Google Cloud AI/ML APIs. Strong proficiency in Java, Spring Boot, and microservices. Advanced knowledge of Databricks for data engineering and analytics. Experience with CI/CD pipelines using GitHub Actions and Harness. Familiarity with DevOps practices, container orchestration (Kubernetes), and automated testing. Understanding of AI governance frameworks and responsible AI practices. Preferred Qualifications Experience in multi‑cloud deployments (Azure + Google Cloud Platform). Exposure to MLOps frameworks (Kubeflow, MLflow). Strong background in data engineering pipelines for AI workloads. Ability to mentor sprint teams in adopting AI‑first practices. For applications and inquiries, contact: View email address on click.appcast.io #J-18808-Ljbffr Openkyber
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