Description
Key Skills: Agentic AI, Agentic Layer A2A Frameworks, MCP Protocol, AI/ML Engineering, Vector Embeddings, Prompt Engineering, Context Engineering, Python, Java, Go, Azure Cloud, Azure AI Search, Redis, Cosmos DB, Azure Functions, Azure Container Apps, Cloud-Native Architecture, Scalability, Performance Optimization, Generative AI, RAG Pipelines, Multi-Agent Systems, LLMOps, MLOps, CI/CD, Model Monitoring
Roles & Responsibilities:
- Build and deploy Agentic AI solutions using Agentic Layer A2A Frameworks and MCP Protocol.
- Apply AI/ML engineering practices including vector embeddings, prompt engineering, and context engineering.
- Design and develop production-grade applications and services using Python, Java, or Go.
- Develop scalable and reliable cloud-native applications following modern architecture principles.
- Deploy and manage applications on Azure Cloud using Azure Functions and Azure Container Apps.
- Integrate and manage AI and data services including Azure AI Search, Redis, and Cosmos DB.
- Design highly scalable systems with a focus on performance optimization and reliability.
- Build and support multi-agent systems, Generative AI use cases, and RAG-based implementations.
- Implement LLMOps/MLOps practices, CI/CD pipelines, and model monitoring frameworks.
- Collaborate with cross-functional teams to deliver AI-driven solutions aligned with business objectives.
- Optimize application performance and ensure efficient resource utilization in cloud environments.
- Ensure solutions align with security, compliance, and regulated data handling requirements when applicable.
Experience Required:
- 4 - 8+ years of experience in AI/ML Engineering, Cloud Engineering, or Software Development.
- Strong hands-on experience in Agentic AI development and A2A frameworks.
- Expertise in vector embeddings, prompt engineering, and context engineering.
- Strong programming experience in Python, Java, or Go.
- Hands-on experience with Azure Cloud services including Azure Functions, Azure Container Apps, Azure AI Search, Redis, and Cosmos DB.
- Experience with cloud-native architecture, scalability, and performance optimization.
- Exposure to Generative AI, RAG pipelines, Multi-Agent Systems, LLMOps, MLOps, and CI/CD practices is preferred.
- Healthcare domain experience and regulated data environment exposure are an added advantage.
Education: Any Graduation