Interested in this AI/ML Engineer role at Day & Zimmermann?
Apply Now →Skills & Technologies
About This Role
About Day and Zimmermann
When you’ve been around for more than a century, you know a thing or two! At Day \& Zimmermann, we take our lifetime of experience and make things better! Our 37,000\+ employees help bring big ideas to life every day. We’re pushing the boundaries of innovation in construction \& engineering, operations \& maintenance, staffing, and security \& defense. And that’s not all. Did we mention that we have 900\+ worldwide locations with $3 billion USD in annual revenue? Come join our in on purpose – We put people to work, we protect American freedoms, and we help our customers power and improve the world. We do what we say.® http://www.dayzim.com
Job Summary
---------------
Responsible for the end\-to\-end design, development, and deployment of advanced AI and machine learning solutions in support of D\&Z AI initiatives. This senior engineering role requires equal expertise in generative AI application development and traditional machine learning, spanning the delivery of LLM\-based agents, RAG systems, MCP (Model Context Protocol) servers, and predictive models to production. The AI/ML Engineer 2 owns complex projects independently, drives technical architecture decisions, evaluates and integrates emerging AI tools and frameworks, and mentors junior team members through code review and technical guidance. This role collaborates directly with cross\-functional stakeholders to scope, design, and integrate AI/ML solutions into business processes, and communicates complex technical concepts clearly to both technical and non\-technical audiences.
This position is open to remote candidates; however, preference will be given to candidates who are local to one of our office locations or willing to relocate to work in a hybrid capacity.
Responsibilities
--------------------
- Designs, builds, and deploys production\-grade generative AI applications including AI agents, RAG chatbots, workflow automations, and MCP servers using modern frameworks and vector databases.
- Designs, develops, and deploys production\-grade predictive models using machine learning techniques across regression, classification, clustering, and time series.
- Authors custom skills, prompts, slash commands, and evaluation harnesses to accelerate team delivery and improve AI application quality, using AI coding tools such as Claude Code, Codex, and Cursor.
- Defines and owns production monitoring, drift detection, online/offline metrics, and retraining strategies for AI/ML systems; applies statistical methods and evaluation frameworks to validate outputs and drive iterative improvement.
- Owns data engineering and pipeline infrastructure underlying AI/ML systems, including retrieval architectures, embeddings, context engineering, and model training/fine\-tuning workflows.
- Reviews code, mentors junior engineers, and contributes to team\-wide engineering standards, documentation, and internal knowledge management patterns. Presents results and delivers live demonstrations of AI/ML solutions to technical and executive audiences.
KSAs (Knowledge, Skills, and Abilities)
-------------------------------------------
- Deep hands\-on experience building production LLM applications with leading providers (Anthropic, OpenAI, Google, open\-source models); expertise in prompt engineering, context engineering, retrieval\-augmented generation (RAG) with vector databases, AI agent design, tool use, and Model Context Protocol (MCP) server development; experience authoring custom skills and building evaluation harnesses for LLM systems.
- Proficiency in ML frameworks (scikit\-learn, XGBoost, PyTorch, TensorFlow); strong foundation in statistical analysis, hypothesis testing, feature engineering, model optimization, deployment, and monitoring of predictive models across regression, classification, clustering, and time series; ability to design and validate experiments, define online/offline metrics, and apply quantitative methods to production problems.
- Strong proficiency in Python as the primary language across ML and AI application development; comfort writing production\-grade, maintainable, well\-tested code.
- Strong SQL skills with expertise in data extraction, cleaning, transformation, and pipeline development for structured and unstructured data; experience deploying ML/AI workloads across on\-premises infrastructure and cloud platforms (AWS, Azure, GCP).
- Proficient in Git, branching strategies, code review, and CI/CD pipelines; experience with containerization, API development, and observability (logging, tracing, cost and token monitoring) for LLM applications; proficient use of AI coding tools (Claude Code, Codex, Cursor) including configuring them, authoring custom skills and commands, and integrating them into team workflows; familiarity with guardrails and security considerations for production AI systems.
- Ability to lead technical discovery with stakeholders, make architecture decisions, review code, mentor junior engineers, and raise the technical bar across the team.
- Skilled at creating dashboards, data visualizations, live demos, and technical presentations to convey complex AI/ML findings to technical and non\-technical stakeholders.
- Experienced in gathering requirements, working in cross\-functional teams, and producing clear documentation, user guides, and technical reports.
Minimum Qualifications
--------------------------
- Bachelor’s Degree in Arts/Sciences (BA/BS) in IT, Computer Science, or related field highly preferred Required
- Will consider 7 years relevant experience in lieu of degree
- 4\+ years of relevant experience
- Great attitude and team player.
- Successful completion of background screening process.
Essential Functions
-----------------------
- Visual acuity (e.g., needed to prepare and analyze data, to transcribe documents, to view a computer, to read, to inspect objects, to operate machinery)
- Manual Dexterity (e.g., picking, pinching, typing, or other working that uses the fingers)
- Grasping (e.g., use of hand to apply pressure)
- Hearing
- Talking
- Capacity to think, concentrate and focus over long periods of time
- Ability to write complex documents in the \[English] language
- Ability to read complex documents in \[English] language
- Capacity to express thoughts orally (e.g., accurately, quick and loudly convey spoken instructions to workers)
- Capacity to reason and make sound decisions
- Ability to regularly perform all job functions at Company’s office or work site
Compensation and Benefits
-----------------------------
In compliance with this state’s pay transparency laws, the salary range for this role is $101,840\.00 \- $165,490\.00\. This is not a guarantee of compensation or salary, as final offer amount may vary based on factors including but not limited to experience and geographic location. (The specific programs and options available to an employee may vary depending on date of hire, schedule type, and the applicability of collective bargaining agreements).
We care about our employees and it shows. Our staff receive a competitive salary and a comprehensive benefits package which includes medical/Rx, dental and vision coverage; life, AD\&D and disability insurance; flexible spending accounts; 100% paid maternity leave for up to 12 weeks, parental leave, family leave, other paid time off; voluntary benefits and discount programs to meet our employees’ individual needs including pet insurance for our furry family members!
Diversity, Inclusion \& Equal Employment Opportunity
Day \& Zimmermann is committed to maintaining an inclusive workforce, where employees are hired, retained, compensated and promoted based on their contributions to our Company. Our collective strength is rooted in over a century of diverse employees and businesses, commitment to success, and delivery on promises made. Federal and state Equal Employment Opportunity laws prohibit employment discrimination based on race, color, religion, sex, sexual orientation, age, national origin, citizenship status, veteran status and disability status. Day \& Zimmermann is committed to providing an equal opportunity work environment in full compliance with these laws. If you are an individual with a disability and you require an accommodation in the application process, please email [email protected], and please specify which position you are interested in, including job title and location.
Salary Context
This $101K-$165K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Day & Zimmermann, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($133K) sits 38% below the category median. Disclosed range: $101K to $165K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Day & Zimmermann AI Hiring
Day & Zimmermann has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US. Compensation range: $165K - $165K.
Location Context
AI roles in Austin pay a median of $214,343 across 143 tracked positions.
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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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.