AI Cloud Solution Architect
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
Drive teams through AI/AGI implementation across SDLC phases and AI-first coding.
Experience with Agentic AI Frameworks
- LangChain/LangGraph
- MS Agent Framework
- CrewAI for custom agent development
- ClientP
Experience with Business Partners & Product Teams
Work with business partners and product teams to ideate, conceptualize, and scale AI solutions.
Exposure to Tools
- Claude Code
- GHCP
- MS Fabric
- Anthropic
- Gemini
- 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.