Senior AI Engineer

Primoris Services Corporation•Texas, TX

21d•Remote

About The Position

We're looking for a Senior AI Engineer to build production-grade AI-enabled capabilities for our enterprise solutions. This role is hands-on and delivery-focused: you'll implement RAG pipelines, agentic systems, and AI integrations using C#/.NET, and build the services, APIs, frontend applications, and data layers that bring those AI capabilities to our users. You'll work under the technical direction of the Lead Azure AI Solution Architect, collaborating on proof-of-concepts when needed, but owning the engineering execution, code quality, and operational readiness of the delivered software. This is an individual contributor role with no direct reports.

Requirements

Bachelor’s degree in computer science, Information Technology, or similar Mathematics
5+ years of professional software engineering experience with strong proficiency in C#/.NET (design patterns, performance, testing, maintainability)
Practical experience implementing generative AI patterns (prompting, RAG) and agentic approaches (agent orchestration, multi-agent patterns, tool usage)
Demonstrated proficiency with Clean Architecture and SOLID principles in production systems
Experience building frontend applications using Blazor, React, or comparable modern frameworks
Experience building API-first services (REST, versioning, documentation, secure-by-default design)
Strong SQL skills including relational database design, stored procedures, query optimization, and data modeling
Experience with NoSQL databases, specifically MongoDB and Azure Cosmos DB
Working knowledge of Azure fundamentals and common services used in AI and enterprise solutions
Azure DevOps experience including YAML pipelines and CI/CD automation
Secure coding experience (authentication, authorization, secrets management, OWASP-aligned practices, code review rigor)
Experience evaluating, implementing, or integrating enterprise AI platforms, tools, or services including large language model providers, AI development frameworks, or AI productivity solutions.
Strong analytical and problem-solving skills with the ability to assess emerging technologies, compare technical approaches, and communicate recommendations to both technical and business audiences.

Nice To Haves

Master’s degree in computer science or a related field
Microsoft Certified: Azure Developer Associate (AZ-204)
Microsoft Certified: Azure AI Engineer Associate (AI-102)
Experience with Microsoft Agent Framework or Semantic Kernel for agent orchestration in .NET
Experience implementing multi-agent systems and agentic RAG patterns in production
Experience with vector search concepts and embedding implementations
Experience fine-tuning and deploying AI models
Familiarity with Model Context Protocol (MCP) for agent interactions
Bicep experience for infrastructure-as-code
Exposure to Copilot Studio or Power Platform integrations
Experience evaluating enterprise AI platforms such as ChatGPT Enterprise, Anthropic Claude, Google Gemini, Microsoft Copilot, Azure AI Foundry, AI Gateways, or similar enterprise AI solutions.
Experience performing AI product evaluations, proof-of-concepts, vendor assessments, and technical due diligence for enterprise AI initiatives.
Familiarity with enterprise AI governance, model risk management, responsible AI practices, and AI security considerations.
Experience supporting enterprise AI adoption initiatives including user enablement, training, documentation, and change management activities.

Responsibilities

Implement AI capabilities. Build RAG pipelines, agentic systems, multi-agent coordination, tool/function calling, and agent orchestration. Implement prompt engineering patterns, integrate with Azure OpenAI and other LLM services, and work with fine-tuned models to deliver production-quality AI features.
Build and maintain C#/.NET services. Implement established solution designs applying Clean Architecture, SOLID principles, and proven patterns to deliver maintainable, testable production code.
Develop frontend experiences. Build responsive, user-friendly interfaces using Blazor, React, or other modern frontend frameworks. Create intuitive UIs that surface AI capabilities to end users effectively.
Implement API-first endpoints and integrations. Build APIs for AI-enabled workflows with clear interface contracts, versioning standards, and comprehensive documentation.
Design and implement database solutions. Create relational database schemas, write stored procedures, and optimize SQL queries. Work with both relational (SQL Server) and NoSQL databases (MongoDB, Cosmos DB) and integrate vector stores for AI retrieval workloads.
Integrate Azure cloud services. Work with identity, networking, data, and AI platform components. Follow enterprise security and compliance guardrails.
Create and maintain CI/CD pipelines. Build Azure DevOps pipelines using YAML. Automate builds, tests, security checks, and deployments. Ensure infrastructure-as-code practices for reproducibility.
Apply secure coding practices. Implement input validation, proper authentication/authorization integration, secrets management, dependency hygiene, and participate in security-focused code reviews.
Ensure code quality and reliability. Write unit and integration tests. Implement comprehensive logging, monitoring hooks, error handling, and performance tuning. Own code quality metrics.
Produce engineering documentation. Create runbooks, deployment notes, and API documentation. Contribute to architecture decision records. Conduct thorough code reviews focused on standards adherence and quality.
Evaluate and implement enterprise AI platforms and tools. Conduct comparative assessments of AI solutions including OpenAI, Anthropic, Google Gemini, Microsoft Copilot, and emerging AI technologies. Partner with architecture, security, and business stakeholders to recommend, prototype, document, and implement enterprise AI capabilities that align with business objectives, governance requirements, and technical standards.
Research emerging AI technologies and industry trends. Continuously evaluate advancements in large language models, agentic frameworks, orchestration platforms, AI gateways, and enterprise AI tooling. Provide recommendations on adoption strategies, technical feasibility, implementation approaches, and operational considerations.
Support AI platform enablement and operationalization. Assist with AI platform configuration, testing, rollout planning, user adoption, documentation, governance processes, and production support activities to ensure successful enterprise deployment.
Collaborate on AI solution architecture and technical strategy. Contribute engineering expertise during technology evaluations, proof-of-concepts, vendor assessments, and roadmap planning efforts. Partner with business leaders, architects, security teams, and platform owners to evaluate, recommend, and implement enterprise AI solutions. Support the full lifecycle from technology assessment and proof-of-concept through production deployment, governance, documentation, and ongoing optimization.