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About This Role
Location: Hybrid Preferred (Minneapolis, MN or Denver, CO) | Remote Considered Employment Type: Full-time
Are you passionate about ensuring AI solutions continue delivering meaningful business value long after deployment? Trissential is seeking an AI Operations Specialist to join our client's team and support the operational success of enterprise AI agents, virtual assistants, and generative AI solutions in production. In this role, you will monitor AI performance, quality, adoption, governance, and business outcomes while partnering with Product, AI Engineering, Operations, and business stakeholders. You will serve as the operational owner of AI solutions in production, ensuring they remain accurate, compliant, effective, and aligned with business objectives.
What's in It for You?
- Be the operational owner of cutting-edge AI solutions used across a large enterprise environment
- Drive measurable improvements in AI quality, adoption, customer experience, and business value
- Collaborate with Product, AI Engineering, Operations, Security, Governance, and business subject matter experts
- Gain exposure to Responsible AI practices, enterprise governance programs, and AI observability platforms
- Work in a highly visible role where your insights directly influence AI strategy and operational outcomes
- Help define long-term AI operational standards and best practices across the organization
- Opportunity to contribute to enterprise-wide AI transformation initiatives
Your Role & Responsibilities
- Own the operational performance of AI-powered solutions after implementation and hypercare.
- Monitor AI quality, response accuracy, relevance, compliance, adoption, and business outcomes
- Track and report on key AI performance metrics, including response quality, customer satisfaction, adoption, escalation rates, and overall solution effectiveness
- Review transcripts and AI conversations to evaluate accuracy, compliance, and overall response quality
- Identify, troubleshoot, and escalate AI performance issues, including inaccurate responses, knowledge gaps, and workflow failures
- Monitor and improve AI knowledge sources by identifying content gaps, validating information accuracy, and ensuring content remains current
- Support AI governance and compliance activities to ensure solutions meet Responsible AI, privacy, and organizational standards
- Coordinate incident investigations and identify root causes across business and technical teams
- Translate operational findings into prioritized backlog items for Product Owners and AI Engineering teams
- Measure and report value realization, including productivity gains, self-service adoption, customer outcomes, utilization metrics, and ROI
- Develop operational procedures, governance standards, and knowledge-transfer documentation to support long-term sustainability
- Drive continuous improvement initiatives that enhance AI performance, reliability, and business impact
- Own the operational performance of AI-powered solutions after implementation and hypercare
- Monitor AI quality, response accuracy, relevance, compliance, adoption, and business outcomes
- Develop and maintain operational scorecards covering containment, satisfaction, escalation rates, retrieval effectiveness, response quality, latency, and utilization
- Review transcripts and AI conversations to evaluate accuracy, grounding, compliance, and overall response quality
- Investigate hallucinations, retrieval failures, misrouted interactions, prompt issues, knowledge gaps, and workflow breakdowns
- Manage and optimize Knowledge Hub performance through content gap analysis, search quality evaluations, source validation, and freshness monitoring
- Support Responsible AI monitoring activities including privacy validation, governance reporting, audit readiness, bias reviews, and guardrail compliance
- Coordinate incident investigations and identify root causes across business and technical teams
- Translate operational findings into prioritized backlog items for Product Owners and AI Engineering teams
- Measure and report value realization, including productivity gains, self-service adoption, customer outcomes, utilization metrics, and ROI
- Develop operational procedures, governance standards, and knowledge-transfer documentation to support long-term sustainability
Skills & Experience You Should Possess
- 5+ years of experience in technology operations, analytics, product operations, quality engineering, knowledge management, conversational AI, or a related field
- Experience supporting AI, conversational AI, digital assistant, chatbot, knowledge management, search, or automation solutions in a production environment
- Experience evaluating solution quality through transcript reviews, response evaluations, quality assessments, or operational performance reviews
- Strong operational analytics experience with dashboards, KPIs, performance reporting, and trend analysis
- Knowledge of search effectiveness, knowledge management, content validation, and content-gap analysis
- Experience investigating production incidents and driving root-cause analysis efforts
- Understanding of Responsible AI principles, governance frameworks, privacy considerations, and compliance monitoring
- Excellent communication skills with the ability to explain operational issues and business impacts to diverse stakeholders
- Strong documentation, organizational, and continuous-improvement capabilities
Bonus Points If You Have
- Experience supporting AI solutions within customer service, customer operations, or contact center environments
- Hands-on experience with Microsoft Copilot Studio, Salesforce Agentforce, enterprise chatbot platforms, or related AI ecosystems
- Experience with AI observability tools, conversation analytics platforms, and evaluation frameworks
- Familiarity with Jira or similar work-management platforms
- Background working within regulated, utility, audit-sensitive, healthcare, financial services, or privacy-focused environments
- Experience supporting enterprise search and retrieval systems
Education & Certifications You Need
- Bachelor's degree in Information Technology, Computer Science, Business Analytics, Data Analytics, Engineering, or a related discipline, or equivalent practical experience
- Relevant certifications in Microsoft technologies, cloud platforms, AI, IT Service Management, Product Operations, Analytics, or Data Management are beneficial
- Certifications are valued but do not replace hands-on production operations experience
What We Offer
At Trissential, we believe talented professionals should have meaningful opportunities to create measurable impact while continuing to grow their careers. By joining our client's team, you'll help shape the future of enterprise AI operations while working in a collaborative and supportive environment.
- Competitive Compensation – $90-$100 per hour. Final compensation is determined based on skill alignment, years of experience, and fair, market-based rates by geography.
- Comprehensive Benefits for you and your dependents – Extensive Medical Plans, Dental, Vision, HSA with company contribution, company sponsored Life Insurance and Short / Long term disability insurance, a host of voluntary plans including legal, hospital, accident, pet wellness, and 401k with matching
- Paid Holidays and Paid Time Off
- Career Development and Advancement – Expleo is a global company in 29 Countries worldwide with a high focus on development
- Strong Team Culture – Collaborate with experienced professionals who value precision, performance, and teamwork
Please note: This position is open only to candidates authorized to work in the United States.
Ready to be part of something exciting?
If you're passionate about operational excellence, AI quality, governance, and ensuring AI delivers measurable business value, we encourage you to apply and help shape the future of enterprise AI operations with Trissential and our client.
Job Type: Contract
Pay: $90.00 - $100.00 per hour
Benefits:
- 401(k)
- Flexible schedule
- Vision insurance
Work Location: Hybrid remote in Minneapolis, MN 55447
Salary Context
This $187K-$208K range is above the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Trissential, 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($197K) sits 7% below the category median. Disclosed range: $187K to $208K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Trissential AI Hiring
Trissential has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Minneapolis, MN, US. Compensation range: $208K - $208K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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
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