AI Solution Architect

AI Solution Architect

Location- Cincinnati OH (Onsite)

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 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.