AI Engineer

Job Summary

The AI Engineer is responsible for building and deploying AI agents (Microsoft Copilot and Claude) and private retrieval-augmented generation (RAG) applications. The AI Engineer possesses deep expertise in private retrieval-augmented generation (RAG) architectures, including data ingestion, embeddings, retrieval, grounding, evaluation, and secure deployment of enterprise AI solutions.

Responsibilities

What you will do

• Design, build, and deploy AI agents using Microsoft Copilot Studio and Claude (Anthropic) for enterprise use cases
• Architect and implement private retrieval-augmented generation (RAG) applications using vector databases (e.g., Pinecone, Weaviate, Azure AI Search, Chroma) — covering ingestion, chunking, embeddings, retrieval, grounding, and evaluation
• Ensure private RAG deployments meet enterprise requirements for data isolation, security, and governance
• Partner with business, data, and product teams to translate requirements into working AI agent and RAG solutions
• Manage the underlying data layer — databases and pipelines — that supports agents and RAG applications
• Design and implement AI-enabled business processes from discovery and solution design through integration, testing, deployment, monitoring, and continuous improvement.
• Where relevant, contribute to automation (RPA), machine learning, and front-end delivery workstreams
• Support enterprise AI governance initiatives by integrating AI solutions with internal systems, external platforms, APIs, workflow engines, data sources, and business applications to enable end-to-end business processes.

Qualifications

• Bachelor's degree in Information Technology, Computer Science, or a related field. Required
• 3-5 years of AI agent development: Experience designing, building, and deploying AI agents using enterprise AI platforms such as Microsoft Copilot Studio and Claude (Anthropic). Experience with both platforms preferred. Required
• 3-5 years of Private RAG Architecture: Hands-on experience developing retrieval-augmented generation (RAG) applications, including vector databases, embeddings, chunking strategies, retrieval optimization, evaluation frameworks, and secure deployment patterns. Required
• 4-6 years of Data Engineering: Experience in database management and data engineering. Required
• 3-5 years of Agent Orchestration: Experience designing multi-step, tool-enabled, and workflow-driven agents that interact with APIs, enterprise systems, knowledge bases, and approval workflows. Required
• 3-5 years of Integration Engineering: Experience with REST APIs, webhooks, middleware, authentication protocols, and enterprise application integrations. Preferred
• 2-4 years of experience with AI/ML concepts, Generative AI technologies, prompt engineering, and evaluation methodologies. Preferred
• 3-5 years of experience implementing evaluation frameworks, observability tools, monitoring solutions, guardrails, and feedback mechanisms to assess AI solution quality, reliability, security, and business impact. Required
• 3-5 years of strong Python development experience, including API integrations, automation, testing, data processing, and AI application development. Required
• 3-5 years of experience with software development lifecycle practices, including Git, CI/CD, automated testing, DevOps methodologies, enterprise security controls, identity management, RBAC, secrets management, and secure deployment practices. Required
• 2-4 years of experience deploying AI solutions in Azure cloud environments, including Azure AI Foundry, Azure AI Search, Azure OpenAI, Azure Functions, and related services. Preferred
• 2–3 years of experience with Microsoft Power Platform solutions, including Power Automate, Power Apps, and workflow automation. Preferred
• 2-3 years of experience developing web-based user interfaces using Angular, React, or similar frameworks. Preferred
• 2-4 years of experience with AI orchestration and agent frameworks such as Semantic Kernel, LangGraph, LangChain, MCP, or similar technologies. Preferred