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About This Role
SPEED · QUALITY · SERVICE
IT Project Manager – AI \& Automation
Department:
Information Technology
Reports to:
Pending — to be determined by Kyle Stout \& Josh Leon
Location:
Remote\-eligible (U.S.) · HQ: Overland Park, KS
Type:
Full\-Time, Exempt
Titan Protection and Consulting is looking for a driven, highly organized, and technically capable professional to join our growing team as an IT Project Manager – AI \& Automation. Titan is a fast\-scaling security and drone\-services company built on three principles — Speed, Quality, and Service. As we grow, we are using AI and automation to absorb the repetitive, everyday work so our people can focus on relationships, judgment, and decision\-making. This role owns the delivery of that work.
Position Summary
Under the direction of Titan leadership, the IT Project Manager – AI \& Automation drives day\-to\-day delivery of Titan's internal AI transformation program (“Streamline”), from intake to adoption. You will turn a pipeline of AI and automation opportunities into shipped, governed, and measurable tools — sequencing pilots, building and maintaining workflows on the Microsoft 365 / Power Platform stack, operating the governance guardrails around them, and driving adoption across departments.
You will work under the technical direction of Titan’s AI partner (DiamondWorks) to execute Streamline’s architecture, while partnering with department heads and executive sponsors to make sure Titan ends up with one connected operating system, not a pile of disconnected tools. The relationship with DiamondWorks itself — scope, contract, deliverables — is held by Titan leadership; this role executes the work under that partner’s technical guidance. This is a blended role: some strong candidates will lean toward hands\-on building, others toward coordinating delivery across people and partners. We are evaluating for strength in either direction, not requiring mastery of both on day one.
Key Responsibilities
- Own the project pipeline. Run intake, prioritization, and sequencing for AI and automation initiatives; maintain the project tracker and delivery cadence so leadership always knows what is on track, off track, and why.
- Deliver adoption pilots end to end. Scope, pilot, and roll out use cases such as an HR self\-service assistant, sales and marketing automations, and virtual\-assistant replacement — measuring hours saved and feeding results into the company’s AI adoption leaderboard.
- Build and maintain on the Microsoft stack. Configure and maintain automations and dashboards using Power Automate, Power BI, Planner, SharePoint, and Dataverse, under DiamondWorks’ technical direction; help integrate source systems (e.g., HubSpot, Sage, Paycom, TrackTik) into a single, trusted source of truth.
- Govern the AI layer. Run the lightweight, repeatable process for how the AI tools and automations touching company systems are accessed, monitored, and checked for accuracy — access controls, audit logging, and escalation routing for the automation itself, enforcing Titan’s Acceptable Use Policy day to day. This role governs the AI/automation layer, not the underlying systems — data governance and policy authorship for Sage, Paycom, HubSpot, and TrackTik stay with their existing owners and with Titan leadership/legal.
- Capture the “how.” Work with department leaders to document processes that currently live in people’s heads, turning them into reproducible workflows that automation and AI agents can reliably run.
- Coordinate delivery partners. Work under DiamondWorks’ technical direction on Streamline builds, and coordinate other vendors as needed; ensure every build is documented and transferable to the internal team — never trapped with one or two people.
- Maintain the scorecard. Keep KPIs, projects, and issues reconciled and current so the weekly leadership meeting solves problems instead of chasing “are you sure?” across spreadsheets.
- Drive adoption and enablement. Train and support end users, champion change management, and remove the friction that keeps good tools from being used.
- Report on impact. Track and communicate program KPIs across effort focus, adoption, hours saved/value, and governance/auditing.
Qualifications
- Bachelor’s degree in Information Technology, Business, or a related field — or equivalent professional experience.
- 3\+ years of experience managing technology, IT, or automation projects, ideally in a fast\-growing or operations\-heavy business.
- Experience with the Microsoft 365 / Power Platform ecosystem — Power Automate, Power BI, SharePoint, Planner, Teams, and Dataverse (or comparable tools) — whether as a hands\-on builder, a technical coordinator directing others, or a mix of both.
- Working familiarity with AI tools and large language models (e.g., Microsoft Copilot, Claude, ChatGPT), including practical prompt design; exposure to retrieval\-augmented generation (RAG) or agent workflows is a plus.
- Demonstrated ability to lead change management and drive user adoption across non\-technical teams.
- Strong operational\-governance mindset for AI and automation tools — comfort setting access controls, audit logging, and accuracy checks on AI\-touched data, and working within client\-contract and service\-reliability commitments.
- Excellent written and verbal communication; able to translate between business needs and technical execution.
- Self\-directed and accountable, with a proven ability to manage priorities and deliver independently in a remote environment.
- Comfort taking technical direction from an external partner while being accountable to an internal supervisor for delivery and conduct.
- Familiarity with a structured operating cadence (e.g., EOS/L10\) is a plus.
- Excellent moral character and clean background free from excessive misdemeanors, traffic offenses, or any felonies, as well as a positive record with creditors and prior employers.
Compensation
$95,000 – $125,000 per year
*Benchmarked against Kansas City IT project manager (\~$129K avg.) and national AI/automation project manager (\~$112K avg.) markets, adjusted for a remote\-eligible, growth\-stage role.*
Benefits
- Medical, dental, and vision insurance for full\-time employees — participation may begin the 1st day of the month following 60 days of employment.
- Aflac accident, hospital, short\-term disability, life, and cancer plans available to full\-time employees.
- Paid time off — accrued after one year of employment.
- Paid holidays.
- Access to the Titan store — purchase equipment, clothing, and firearms at a discounted rate.
- Referral bonuses — cash or store credit for successfully hired referrals.
- Direct deposit to the bank account(s) of your choice.
- Verizon discounts — up to 18% off your monthly cell phone bill.
Ready to build the future of how Titan works?
Apply through the Titan Protection careers portal at tpcsecurity.applicantpro.com/jobs or submit an employment inquiry at tpcsecurity.com/employment\-inquiries.
*Titan Protection and Consulting, Inc. is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected veteran status, or any other characteristic protected by law. Employment is contingent upon successful completion of a background check and any licensing required for the role.*
Pay: $95,000\.00 \- $125,000\.00 per year
Benefits:
- 401(k)
- Dental insurance
- Employee discount
- Health insurance
- Paid time off
- Referral program
- Vision insurance
Work Location: Hybrid remote in Overland Park, KS 66212
Salary Context
This $95K-$125K 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 Titan Protection and Consulting, 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 $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 ($110K) sits 49% below the category median. Disclosed range: $95K to $125K.
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.
Titan Protection and Consulting, Inc. AI Hiring
Titan Protection and Consulting, Inc. has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Overland Park, KS, US. Compensation range: $125K - $125K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 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
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