Interested in this AI/ML Engineer role at Primoris Services Corporation?
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About This Role
Job Overview:
We're looking for an 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, Databricks AI and analytics solutions, and AI integrations using C\#/.NET, while building the services, APIs, frontend applications, and data layers that bring those 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, data\-to\-AI implementation, and operational readiness of the delivered solutions. This is an individual contributor role with no direct reports.
Please note: Although a specific location is displayed in our applicant tracking system, this position is fully remote and may be performed from anywhere within the United States.What You'll Do
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- 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, Databricks AI capabilities, and enterprise AI tooling. Remain actively engaged with new and upcoming AI trends, including newly released Databricks technologies, and 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.
- Build Databricks AI and analytics solutions. Develop production\-ready AI capabilities on the Databricks platform, including AI/BI Genie experiences, governed natural\-language analytics, retrieval workflows, model and agent integrations, and reusable services that connect enterprise data to AI\-powered business outcomes.
- Transform Gold\-layer data into AI\-ready analytics. Partner with data engineering and business teams to consume curated Gold\-layer data, define semantic and analytical structures, prepare trusted datasets for AI use cases, and turn enterprise data into scalable AI analytics, insights, and conversational experiences.
- Operationalize governed Databricks AI workloads. Implement and support secure, reliable Databricks AI solutions using appropriate platform capabilities for data governance, vector retrieval, model lifecycle management, monitoring, evaluation, deployment, and production support while following enterprise architecture and security standards.
Required Qualifications
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- 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.
- Hands\-on experience with Databricks for enterprise data, analytics, machine learning, or generative AI workloads, including the ability to build solutions that use curated Silver\- and Gold\-layer data.
- Experience developing AI\-powered analytics or conversational data experiences using Databricks AI/BI Genie or comparable natural\-language business intelligence and semantic analytics capabilities.
- Working knowledge of Databricks AI and machine learning capabilities such as governed data access, vector search and retrieval, model or agent serving, MLflow\-based lifecycle management, evaluation, monitoring, and production operationalization.
Nice to Have
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- 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.
- Databricks certification or advanced hands\-on experience with Databricks Data Intelligence Platform capabilities.
- Experience designing semantic models, trusted analytical datasets, or business\-facing AI/BI Genie spaces using curated Gold\-layer data.
- Experience with Databricks\-native generative AI, agent, vector search, model serving, governance, observability, or evaluation capabilities in production environments.
About the Team
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You'll join the IT Corporate Analytics \& Automation team, which is leading Primoris' enterprise AI strategy. We're a collaborative group that values clean code, thoughtful engineering, and pragmatic approaches to AI adoption. This role offers the opportunity to work on cutting\-edge AI implementations while growing your expertise in agentic systems and enterprise\-scale AI solutions built on the .NET platform.
PAY EQUITY
$140,000 \- $150,000
Primoris Services Corporation provides the following compensation range and general description of other compensation and benefits that it in good faith believes it might pay and/or offer for this position. This compensation range is based on a full\-time schedule. Primoris Services Corporation reserves the right to ultimately pay more or less than the posted range and offer additional benefits and other compensation, depending on circumstances not related to an applicant’s sex or other status protected by local, state, or federal law.
\#LI\-JF1
Company Overview:
Primoris Services Corporation is a premier specialty contractor providing critical infrastructure services to the utility, energy, and renewables markets throughout the United States and Canada. Built on a foundation of trust, we deliver a range of engineering, construction, and maintenance services that power, connect, and enhance society. On projects spanning utility\-scale solar, renewables, power delivery, communications, and transportation infrastructure, we offer unmatched value to our clients, a safe and entrepreneurial culture to our employees, and innovation and excellence to our communities. To learn more, visit www.prim.com and follow us on social media at @PrimorisServicesCorporation.Benefits:
- 401k w/employer match
- Health/Dental/Vision insurance plans
- Paid time off
- 10 paid holidays
- Stock purchase plan
EEO Statement
We are an equal opportunity employer, and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veteran status, or any other characteristic protected by law.
Agency Statement
We are not accepting resumes from Third Party Recruiting Firms for this position. If you are an Agency or Search firm representative, contact the Primoris Talent Acquisition Manager directly for consideration. Primoris or its subsidiaries will not be responsible for any fees arising from the use of resumes and online response forms through this source. In addition, Primoris or its subsidiaries will not be responsible for any fees on unsolicited resumes that are submitted to any member of the Staffing or Operations team. Primoris has established an approved vendor program for this service and will only consider accepting submissions from those approved firms. For consideration in becoming an approved vendor, contact HR.
Equal Opportunity Employer
This employer is required to notify all applicants of their rights pursuant to federal employment laws. For further information, please review the Know Your Rights notice from the Department of Labor.
Salary Context
This $140K-$150K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Primoris Services Corporation, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($145K) sits 34% below the category median. Disclosed range: $140K to $150K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Primoris Services Corporation AI Hiring
Primoris Services Corporation has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in TX, US. Compensation range: $150K - $150K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
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