Interested in this AI/ML Engineer role at Blattner Company?
Apply Now →Skills & Technologies
About This Role
A DAY IN THE LIFE
The Lead AI Engineer is the senior technical owner of one of Blattner's core AI platforms, with the first assignment being the Construction Ops Platform. This role is both architect and principal builder: it sets the technical design, writes production code, and remains accountable for the platform after launch, including adoption against a stated target and the cost to run and extend it. The role partners closely with a Senior AI Product Manager and provides technical direction to AI Engineers, Data Engineers, and Full Stack Application Developers.
Blattner's AI work runs through a small central team that owns strategy, governance, and platform engineering, with domain expertise and change management increasingly sitting inside the business. This role sits on the central platform side of that model. Success means the platform is something Blattner can enhance for years, and that engineers within or external to this team can extend it.
STEP INTO THE ROLE
- Own end-to-end technical architecture and delivery of a core software platform, from prototype through production and long-term enhancement.
- Build production LLM and agentic applications: retrieval over project and construction data, agent and tool orchestration, evaluation harnesses, context engineering, and human-in-the-loop review paths.
- Integrate AI solutions with enterprise systems of record (Procore, Salesforce, Bluebeam, ERP and finance systems) and with Blattner's Microsoft Fabric data foundation.
- Establish engineering standards across the AI team: repository structure, testing and model evaluation, release process, observability, consumption and cost monitoring, and security review.
WHAT YOU’LL NEED
- Bachelor’s degree in Computer Science, Data Science, or related field
- 8 or more years of applicable professional experience in Machine Learning, AI Engineering, or related fields.
WHAT SETS YOU APART
- Production experience with LLM application patterns: retrieval, agents and tool use, MCP or comparable tool-calling frameworks, and systematic evaluation
- Deep understanding of cloud and data platforms, with Azure and Databricks preferred, including Azure AI Foundry
- Primarily hybrid role, open to remote for strong candidate in this skill set.
HOW WE INVEST IN YOU
- Competitive pay
- 100% employer paid HDHP insurance premiums for employees
- 401(k) with company match
- HSA and FSA options
- Dental and Vision insurance
- Education Assistance (Tuition Reimbursement)
- Work/Life balance
- Employee/Family focused culture
- Gym on site
- And more!
Blattner is not able to sponsor H-1B visas or permanent residency for this or any position. We are able to consider candidates who hold valid, independent work authorization (e.g., U.S. citizenship, permanent residency, or other valid, non-expired work authorization).
$133,847.00 - $194,078.00 Individual Compensation to be determined based on the applicant's applicable experience and qualifications balanced against internal equity. WHO ARE WE?
With more than 115 years of construction experience, Blattner has played a vital role in building America’s critical infrastructure, ranging from railroads and highways to dams and mines. Since 1997, the company has focused on renewable energy solutions and is responsible for some of the most significant solar, wind and storage projects in the U.S. Today, Blattner has installed over 20% of America’s utility-scale wind, solar and energy storage capacity. Blattner Company is the parent company of Blattner Energy and D.H. Blattner & Sons, of which all are members of the Quanta Services family of companies. Company headquarters are in Avon, Minnesota.
Blattner is an Equal Employment Opportunity Employer committed to diversity and inclusion in the workplace. Blattner will not tolerate discrimination against any applicant for employment or employee on the basis of age (40 and over), ancestry, color, religious creed, gender, gender expression, gender identity, genetic information, marital status, medical condition, mental disability, military or veteran status, national origin, physical disability, race, receipt of public assistance, sex, sexual orientation, or any other status protected by federal, state, or local laws. This commitment governs all aspects of employment including, but not limited to: hiring, promotion, demotion, transfer, layoff, termination, compensation, discipline, and access to benefits and training. Additionally, Blattner is committed, in accordance with applicable law, to making reasonable accommodations for job applicants and employees based on a qualifying disability, a sincerely held religious belief or practice, and for pregnancy, childbirth or related health conditions. Applicants and employees who feel they have experienced or witnessed discrimination or who wishes to request a reasonable accommodation should contact the Human Resources Department. Blattner prohibits retaliation against any individual for making a complaint under this policy, for opposing discrimination, for participating in an investigation pursuant to this policy, or for requesting a reasonable accommodation.
Salary Context
This $133K-$194K range is below the median for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Blattner Company, 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 $212,000 based on 11,857 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($163K) sits 23% below the category median. Disclosed range: $133K to $194K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Blattner Company AI Hiring
Blattner Company has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $194K - $194K.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% of all AI roles offer remote work.
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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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.
Frequently Asked Questions
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.