Interested in this AI/ML Engineer role at Trece, Inc?
Apply Now →Skills & Technologies
About This Role
POSITION SUMMARY
The AI Solutions Engineer serves as Trécé's internal AI capability leader, responsible for identifying, implementing, and continuously improving AI\-powered solutions across all departments. This role bridges the gap between rapidly evolving AI technology and Trécé's specific operational needs — spanning flow chemistry plant automation, laboratory research, regulatory compliance, field application tools, and business administration. The position requires a technically grounded individual who can translate complex AI capabilities into practical value for chemists, engineers, biologists, and administrative staff alike.
POSITION RESPONSIBILITIES
AI Strategy \& Implementation
- Conduct systematic assessments of current business processes across all departments to identify high\-value opportunities for AI, ML, and LLM integration.
- Design, test, configure, and deploy AI/ML/LLM solutions tailored to Trécé's technical and operational contexts, including laboratory workflows, manufacturing plant monitoring, field applications, and administrative functions.
- Document and compare current ('as\-is') and AI\-enhanced ('to\-be') processes, including changes to data pipelines, systems, and user roles.
- Evaluate and recommend consolidated AI platform solutions (e.g., enterprise LLM management platforms) to provide centralized access, cost control, and security oversight across multiple AI tools.
- Establish and enforce guardrails, prompt standards, and validation practices to minimize hallucinations and ensure reliability of AI outputs.
Plant Automation \& Engineering Support
- Collaborate with engineering and chemistry teams to develop AI\-driven monitoring and process control systems.
- Support the design and implementation of agentic automation pipelines to connect plant sensors, data storage, and decision\-support systems.
- Assist in developing predictive maintenance tools that analyze historical operational data to flag equipment wear patterns and preempt failures.
Research \& Laboratory Applications
- Assist researchers in applying AI for data analysis, literature synthesis, report writing, simulation development and experimental design acceleration while maintaining scientific accuracy standards.
- Build or customize domain\-specific AI tools or GPT extensions tuned to Trécé's specialized scientific disciplines.
Field \& Commercial Tool Development
- Lead AI\-assisted development and improvement of field application tools, ensuring field tools are accurate, mobile\-friendly, data\-persistent, and capable of scaling to serve distributor and dealer networks worldwide.
Training, Adoption \& Governance
- Create training materials and conduct hands\-on sessions to build AI proficiency across all departments, with content appropriate to each team's technical background and use cases.
- Track AI tool usage, adoption metrics, and ROI indicators; gather user feedback to drive iterative improvements.
- Maintain company\-wide AI usage policies, cost controls, and access governance.
- Serve as the internal point of contact for AI vendor relationships and security reviews.
Continuous Learning \& Technology Scouting
- Actively monitor AI developments across relevant platforms (LLMs, agentic tools, no\-code automation, scientific AI) and proactively brief teams on applicable advances.
- Evaluate emerging AI platforms and frameworks before broad deployment, including proof\-of\-concept testing and comparative analysis.
General IT Support (Backup)
- Provide backup IT administrator support as needed, including desktop/workstation troubleshooting, RDS/VPN maintenance, printer setup, and software support for ERP and related systems.
*NOTE: This is a long\-term position expected to evolve with the company's growth, AI and automation landscape. Responsibilities can shift as capabilities mature and new applications emerge.*
QUALIFICATIONS \& REQUIREMENTS
Education
- Bachelor's degree required in Computer Science, Software Engineering, Mechanical Engineering, Chemical Engineering, or a related STEM discipline. Master's degree a plus.
- A broad STEM foundation is strongly preferred over highly specialized single\-discipline training — the role requires cross\-functional communication with organic chemists, chemical ecologists, engineers, and business staff.
Technical Skills
- Demonstrated hands\-on proficiency with major LLM platforms (Claude, ChatGPT/GPT\-4, Gemini) and a clear understanding of their respective strengths, limitations, and appropriate use cases.
- Experience with no\-code/low\-code automation platforms such as Zapier, Make, or equivalent agentic workflow tools.
- Proficiency in Python; familiarity with MATLAB, R, or similar scientific computing languages is a plus.
- Experience with or strong aptitude for building custom GPTs, AI agents, or domain\-specific LLM extensions.
- Knowledge of prompt engineering best practices, including techniques for reducing hallucinations and improving output reliability (e.g., chain\-of\-thought prompting, confidence rating, assumption declaration).
Professional Competencies
- Fast, self\-directed learner who can rapidly evaluate new tools and technologies as the AI landscape evolves
- Strong analytical and problem\-solving skills; able to assess ROI, identify automation opportunities, and prioritize high\-impact projects.
- Excellent cross\-functional communicator
- Highly organized with the ability to manage concurrent projects across multiple departments
- Intellectually honest about AI limitations; applies appropriate skepticism and validation discipline when evaluating AI\-generated outputs.
KEY MEASUREMENTS / ACCOUNTABILITY
- Timely and successful delivery of AI implementation projects measured against defined business and technical requirements.
- Measurable, documented improvements in operational productivity, time savings, or cost reduction resulting from deployed AI solutions.
- Quality and adoption rate of AI training programs across departments.
- Reliability and accuracy of deployed AI tools, including hallucination reduction metrics and user satisfaction.
JOB INTERFACE
This position serves as a technical bridge across the entire organization. It requires close, ongoing collaboration with the Principal Engineer, laboratory directors, plant operations, regulatory/ compliance staff, and finance/administration. Regular interaction with external AI vendors, platform providers, and security consultants is expected.
TRAVEL
- Minimal under normal circumstances
Pay: From $75,000\.00 per year
Benefits:
- 401(k)
- 401(k) matching
- Dental insurance
- Employee assistance program
- Health insurance
- Life insurance
- Paid time off
- Retirement plan
- Vision insurance
Application Question(s):
- Will you now or in the future require sponsorship for employment authorization?
- What AI platforms have you used? List all that apply.
- Do you have experience using Python?
- Have you implemented AI\-powered tools, workflows, or automations?
- Have you completed academic, personal, or professional projects involving AI, custom GPTs, AI agents, or workflow automation?
- Briefly describe an AI project you've built or contributed to. This can be academically or professionally.
- Do you have hands\-on experience with LLM platforms such as ChatGPT, Claude, or Gemini?
- Have you used no\-code/low\-code automation platforms such as Zapier or Make?
Education:
- Bachelor's (Required)
Experience:
- AI tools (professionally or academically): 2 years (Required)
- Manufacturing and/or Laboratory Environments: 3 years (Preferred)
Ability to Relocate:
- Adair, OK 74330: Relocate before starting work (Required)
Work Location: In person
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 Trece, Inc, 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.
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.
Trece, Inc AI Hiring
Trece, Inc has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Adair, OK, US.
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.
Frequently Asked Questions
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.