AI Engineer

Job Summary

We are seeking an experienced AI Engineer to design, build, and deploy production-grade agentic AI solutions for enterprise-scale use cases. The ideal candidate will have strong hands-on experience with Agent-to-Agent frameworks, Model Context Protocol integrations, AI/ML engineering, cloud-native architecture, and Azure-based deployment.

Roles and Responsibilities

• Design, build, and maintain multi-agent AI systems using Agent-to-Agent frameworks, including agent roles, communication contracts, orchestration patterns, and lifecycle management.
• Implement and enhance Model Context Protocol integrations to support standardized context sharing, memory management, and tool access across distributed AI pipelines.
• Develop embedding pipelines, prompt engineering strategies, and context engineering approaches for retrieval-augmented generation and LLM-based applications.
• Build production-grade AI services, ML inference wrappers, APIs, and asynchronous components using Python, Java, and/or Go.
• Deploy and manage scalable AI workloads on Azure using Azure Functions, Azure Container Apps, API Management, Event Grid, Service Bus, and related cloud services.
• Design and optimize data and storage solutions using Azure AI Search, Redis, Cosmos DB, Blob Storage, and related vector or caching technologies.
• Apply cloud-native architecture principles to ensure scalability, resiliency, performance optimization, observability, and cost efficiency across AI platforms.
• Collaborate with engineering, product, data science, and DevOps teams to deliver reliable AI solutions, technical documentation, architecture decisions, and stakeholder updates.

Required Qualifications

• Hands-on experience with Agentic Layer, Agent-to-Agent frameworks, and Model Context Protocol.
• Strong AI/ML engineering experience, including vector embeddings, prompt engineering, context engineering, and retrieval-augmented generation concepts.
• Strong programming experience in at least two of the following languages: Python, Java, and Go.
• Proven experience deploying cloud-native solutions on Microsoft Azure.
• Experience with Azure AI Search, Redis, Cosmos DB, and related database or vector search technologies.
• Experience designing and managing Azure Functions and Azure Container Apps.
• Strong understanding of cloud-native architecture, scalability, performance optimization, monitoring, and distributed system design.
• Excellent communication skills with the ability to explain complex technical concepts to technical and non-technical stakeholders.

Preferred Qualifications

• Experience with Azure Blob Storage and document ingestion pipelines.
• Exposure to Iceberg-based lakehouse patterns for large-scale training data management or offline AI evaluation datasets.
• Experience with emerging agentic frameworks such as LangGraph, AutoGen, CrewAI, or custom multi-agent frameworks.
• Experience with Infrastructure-as-Code tools such as Bicep, Terraform, or ARM templates.
• Experience with distributed tracing, structured logging, metrics dashboards, load testing, and capacity planning.