AI &Intelligent Integration Developer

Job Description:

• Integrate enterprise AI platforms, Large Language Models (LLMs), and AI-powered services into business applications and workflows.
• Build AI-enabled solutions using technologies such as Vertex AI, Gemini, OpenAI, Anthropic Claude, or equivalent platforms.
• Implement Retrieval-Augmented Generation (RAG), AI agents, intelligent workflows, and API-based AI integrations.
• Leverage AI-assisted engineering tools to improve software development productivity and quality.
• Collaborate with architecture, data, and AI teams to deliver scalable, secure, and responsible AI solutions.
• Ensure AI integrations comply with enterprise security, governance, privacy, and Responsible AI standards.

Operational Excellence

• Troubleshoot and resolve complex issues across APIs, integrations, cloud platforms, and event-driven systems.
• Support production deployments, incident resolution, and root-cause analysis.
• Optimize solution performance, scalability, reliability, and maintainability.
• Contribute to reusable frameworks, engineering standards, and continuous improvement initiatives.

Experience

• 7+ years of hands-on experience developing enterprise APIs, microservices, and integration solutions.
• Strong experience with Java (Spring Boot) and Python development.
• Experience building event-driven and real-time integration solutions using Kafka and messaging technologies.
• 5+ years of experience developing and deploying cloud-native solutions, preferably on GCP.
• Experience with API management platforms such as Apigee, Kong, MuleSoft, or equivalent technologies.
• Strong knowledge of API security, OAuth, JWT, and secure software development practices.
• Experience integrating enterprise AI platforms such as Vertex AI, Gemini, OpenAI, Anthropic Claude, Azure AI Services, or equivalent technologies.
• Experience developing AI-enabled applications, RAG solutions, AI agents, or intelligent workflow automation.
• Understanding of AI governance, security, privacy, and Responsible AI practices.

Technical Skills

• Java (Spring Boot) and Python
• APIs, RESTful services, microservices, and enterprise integration patterns
• Apigee, Kong, MuleSoft, or similar API management platforms
• Kafka, Pub/Sub, event-driven architecture, messaging, and real-time streaming
• Kubernetes, Docker, and cloud-native application development
• Google Cloud Platform (GCP), including Cloud Run, Cloud Functions, and Pub/Sub
• OAuth, JWT, API security, and secure development practices
• CI/CD, DevOps, and automation
• Distributed systems, scalability, resiliency, monitoring, and observability
• Vertex AI, Gemini, OpenAI, Anthropic Claude, Azure AI Services
• Large Language Models (LLMs), prompt engineering, embeddings, and AI integration patterns
• Retrieval-Augmented Generation (RAG), vector databases, AI agents, intelligent workflows, and MCP
• AI-assisted development tools such as GitHub Copilot, Gemini CLI, and Cursor
• AI governance, Responsible AI, security, privacy, and compliance