AI Cloud Solution Architect

AI Cloud Solution Architect

Location- Remote

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, GCP 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 ClientP.

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 GCP).

• 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. Pay rate: 150000-180000 USD per Year Job type: Direct Placement Division: eTeam Inc (US) Category: eTeam United States Reference: 26-50781