AI Solutions Engineer

Job Title: AI Solutions Engineer

About Verterim:

Verterim is a Governance, Risk and Compliance company focused on helping organizations build, promote and mature their GRC programs. We take a business process view of GRC, with a focus on rapid development based on experience yielding quick time to value for our clients. We are driven by highly skilled, qualified employees combined with practiced industry expertise, matching the technical skills to manage the platform with the business skills needed to develop and implement client-specific use cases.

Job Summary

We are seeking a highly motivated and technically skilled AI Solutions Engineer to join our growing AI engineering team. As part of the AI Solutions Engineering Group, you will collaborate closely with the AI Team Lead to design, develop, and deploy intelligent agents and AI-driven applications across Microsoft’s AI ecosystem.

This role is ideal for an engineer passionate about building scalable, secure, and responsible AI systems using Azure AI Foundry, AutoGen, Semantic Kernel, and Microsoft Power Platform. You will contribute to the full lifecycle of AI agent creation - from design and development to testing, deployment, and continuous improvement - supporting industry and customer-driven solution requirements.

Key Responsibilities

AI Development and Engineering

Design and implement RAG (Retrieval-Augmented Generation) systems for document processing, policy analysis, and compliance automation Build and deploy AI applications using Azure OpenAI, Azure AI Foundry, and Azure Cognitive Services Develop multi-agent systems using frameworks like AutoGen, Semantic Kernel, or LangGraph Create production-grade REST APIs using Python (FastAPI) for AI services Implement prompt engineering strategies including few-shot learning, chain-of-thought, and structured output generation Integrate AI solutions with enterprise data sources (databases, document repositories, external APIs) Optimize LLM performance including context window management, token optimization, and response quality Handle LLM security including prompt injection prevention, guardrails, and content filtering Deploy and maintain AI infrastructure using Azure services and CI/CD pipelines (Bicep, Azure DevOps) Troubleshoot and resolve production issues in AI systems  

Collaboration and Delivery

Collaborate with front-end teams (Next.js) to deliver complete solutions Collaborate with product managers, and business analysts to translate customer needs into actionable AI requirements. Contribute to building reusable frameworks, orchestration templates, and integration components for rapid AI solution deployment. Participate in code reviews, sprint planning, and Agile ceremonies to ensure consistent quality and high-velocity delivery. Assist in preparing technical documentation, deployment guides, and maintenance procedures. Work closely with senior engineers to integrate AI models into production applications, ensuring performance, reliability, and scalability.  

Innovation and Continuous Improvement

Stay current with Microsoft’s evolving AI ecosystem to identify opportunities for improvement and innovation. Propose enhancements to existing AI solutions through improved orchestration, automation, or model optimization. Participate in internal knowledge sessions to deepen expertise in LLM integration, prompt engineering, RAG implementations, and AI governance. Support experimentation with complex multi-agent coordination using AutoGen and Semantic Kernel.  

Qualifications

Required:

2+ years of hands-on experience using Azure OpenAI, Azure AI Foundry, and Azure Cognitive Services. Strong Python programming skills with production code experience Experience building RAG systems with vector databases (Azure AI Search, Pinecone, Weaviate, or similar) Working knowledge of LLM APIs (Azure OpenAI, OpenAI) including parameters like temperature, top-p, frequency penalty Experience with at least one API framework (FastAPI, Flask, or Django) Understanding of embeddings, chunking strategies, and retrieval optimization Familiarity with prompt engineering techniques (few-shot, chain-of-thought, structured outputs) Experience with async programming in Python Basic understanding of Azure cloud services Ability to debug and troubleshoot AI systems in production  

Preferred Qualifications:

Experience with multi-agent frameworks (AutoGen, Semantic Kernel, LangGraph, CrewAI) Experience with Azure AI Foundry and Azure Machine Learning Knowledge of fine-tuning LLMs Familiarity with GRC, compliance, or regulated industry domains Experience with Next.js or TypeScript Microsoft Azure certifications (AI Engineer Associate, Solutions Architect) Experience with CI/CD pipelines and Infrastructure as Code (Bicep, Terraform)  

Tech Stack

Languages: Python (primary), TypeScript/Node.js Backend: FastAPI Frontend: Next.js AI/ML: Azure OpenAI, Azure AI Foundry, Azure AI Search, Azure Cognitive Services Agent Frameworks: AutoGen, Semantic Kernel Cloud: Microsoft Azure Deployment: Bicep, Azure DevOps CI/CD  

Additional Information

Location: Fully Remote Reports To: AI Solutions Engineer – Team Lead Collaborates With: CTO, Developers, Product Managers, Solution Architects Travel: Minimal