Description

You will design and deploy scalable AI solutions, modernizing legacy applications by embedding AI/ML capabilities while maintaining backward compatibility. This role requires architecting AI modules within Java/Spring Boot ecosystems and building robust data pipelines for complex workloads.

This role is on-site.

Responsibilities

• Architect AI modules within Java/Spring Boot applications, ensuring performance, maintainability, and integration with containerized microservices.
• Design and deploy scalable AI solutions leveraging Azure Cognitive Services and GCP Vertex AI across multi-cloud environments.
• Build and optimize data pipelines using Databricks, integrating structured and unstructured data sources for AI workloads.
• Implement CI/CD pipelines using GitHub Actions and Harness to streamline AI model deployment and application releases.
• Establish automated testing frameworks for AI models, ensuring fairness, robustness, and compliance with governance standards.

Required Skills

• 5+ years of experience in AI/ML architecture and cloud-native design.
• Strong proficiency in Java and Spring Boot for building microservices.
• Hands-on experience with Azure AI services and Google Cloud AI/ML APIs.
• 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.
• Bachelor's degree in a relevant technical field.

Preferred Skills

• Experience with Agentic AI frameworks (LangChain, LangGraph, CrewAI, MS Agent Framework) and MCP.
• Exposure to MLOps frameworks (Kubeflow, MLflow) and multi-cloud deployments.
• Ability to mentor sprint teams in adopting AI-first practices and Agentic AI implementation.

Key Skills

Education

Bachelor's degree