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About This Role
About Overwatch Mission Critical
Overwatch is a service\-disabled Veteran\-owned small business (SDVOB) certified through the national veterans business development council (NVBDC), offering construction professional services, talent acquisition, and general contractor for the mission\-critical infrastructure industry. Our mission is the construction and management of state\-of\-the\-art data centers with the precision and reliability this industry demands. From high\-end engineers to seasoned professionals, we deploy the people you need to get your data center off the ground. At Overwatch, it's more than a job. It's purpose. Overview
The Machine Learning (ML) Project Manager will be responsible for overseeing the planning, procurement, delivery, installation, coordination, and scheduling of mission\-critical infrastructure supporting ML and AI deployments within data center environments. This role will serve as the primary point of contact between clients, vendors, contractors, and internal stakeholders to ensure the successful execution of projects involving high\-density compute and supporting mechanical and electrical infrastructure.
This role is ideal for a Project Manager with a strong mission\-critical background who can successfully coordinate procurement, installation, and scheduling activities associated with advanced AI and machine learning infrastructure deployments. Key Responsibilities* Manage all phases of project execution from planning through closeout for machine learning and AI infrastructure deployments.
- Coordinate material procurement, tracking, expediting, and delivery schedules to ensure alignment with project milestones.
Oversee the installation, commissioning, and turnover of mission\-critical equipment including:* In\-row cooling systems
- Cooling racks and associated mechanical equipment
- UPS (Uninterruptible Power Supply) systems
- ATS (Automatic Transfer Switches)
- Chilled water and refrigerant piping systems
- Drip pans and condensate management systems
- Building Management System (BMS) and environmental controls
- Electrical distribution and supporting infrastructure
* Develop and maintain detailed project schedules while identifying and mitigating schedule risks.
- Collaborate with engineering, construction, operations, and commissioning teams to ensure project objectives are achieved safely, on time, and within budget.
- Track procurement status, lead times, logistics, and delivery schedules for critical equipment and materials.
- Facilitate coordination meetings with clients, vendors, contractors, and field teams.
- Manage change orders, RFIs, project documentation, and status reporting.
- Monitor project budgets and support cost\-control initiatives throughout execution.
- Ensure compliance with all safety, quality, and operational requirements.
- Provide regular project updates to leadership and stakeholders, including schedule performance, procurement status, and risk assessments.
Required Qualifications* Bachelor's degree in Engineering, Construction Management, Project Management, or related field preferred.
- 5\+ years of experience managing mission\-critical, data center, industrial, mechanical, or electrical construction projects.
- Demonstrated experience managing equipment procurement and logistics for large\-scale infrastructure projects.
- Strong understanding of mechanical and electrical systems within data center environments.
- Experience managing project schedules using Microsoft Project, Primavera P6, or similar scheduling tools.
- Proven ability to coordinate multiple vendors and stakeholders in a fast\-paced environment.
- Strong communication, organizational, and problem\-solving skills.
Preferred Qualifications* Experience supporting AI, machine learning, high\-performance computing (HPC), or hyperscale data center deployments.
- Knowledge of liquid cooling, high\-density cooling solutions, and advanced thermal management systems.
- PMP certification or equivalent project management credentials.
- Familiarity with commissioning and startup processes for mission\-critical facilities.
Success Metrics* On\-time procurement and delivery of critical equipment.
- Achievement of project milestones and schedule commitments.
- Successful installation and commissioning of ML infrastructure.
- Effective management of project budgets and change control.
- High client satisfaction and stakeholder engagement.
Required Training* Works w/design and field team.
Required Experience* Seasoned Mechanical field engineer.
Data Center Years Experience* 10 Years
Driver’s License Requirement:
For roles that require on\-site presence or travel between work locations as an essential function of the position, candidates must possess and maintain a valid driver’s license appropriate for the role and be eligible to operate a motor vehicle in accordance with applicable laws. Communication Skills:
Demonstrated ability to communicate effectively in a professional work environment, including preparing written reports and documentation, conveying information clearly to team members, clients, and stakeholders, and responding appropriately to verbal and written communications related to job duties. Physical Requirements and Work Environment
This position operates primarily in an active data center construction environment, encompassing both indoor and outdoor work settings. The role requires the physical ability to perform essential job functions safely and effectively in a dynamic environment that includes ongoing construction activity and evolving site conditions.
The work environment includes areas and temporary structures owned, leased, or controlled by third parties over which Overwatch may have limited or no control. Because the project remains under construction, certain areas of the site — including access routes, staging zones, and temporary structures — may not yet be fully ADA\-compliant until construction is complete or those third\-party areas are finalized. Physical Requirements:* Must be able to walk, stand, stoop, twist, bend, and climb stairs or ladders for extended periods while navigating uneven, unpaved, or obstructed terrain.
- Must be able to lift, carry, push, or pull up to 50 pounds on an occasional basis.
- Must be able to drive between job sites and access all areas of an active construction zone, including raised platforms, scaffolding, and confined spaces.
- Must be able to tolerate exposure to outdoor weather conditions, dust, and construction\-related noise, vibrations, and odors.
- Must be able to communicate effectively with contractors, engineers, and site personnel, and maintain situational awareness in high\-activity environments.
- Personal protective equipment (PPE), including hard hats, safety vests, hearing protection, and steel\-toe boots, is required.
- Must have sufficient visual acuity to read drawings, specifications, and safety signage, and to observe site activity.
Work Environment:
Work is performed primarily in and around active construction areas, which may include unfinished buildings, temporary field offices, staging areas, and partially completed infrastructure. These locations may contain uneven surfaces, limited accessibility, and other conditions typical of construction projects prior to final completion. Overwatch does not control and cannot modify accessibility conditions within areas, facilities, or temporary structures owned or managed by third parties. Reasonable Accommodation:
Reasonable accommodations may be made to enable qualified individuals with disabilities to perform the essential functions of this position, provided such accommodations do not create safety hazards, impede essential mobility within active work zones, or require modification of facilities not owned or controlled by Overwatch. Benefits:
We offer a competitive salary and benefits package, including health insurance, dental insurance, vision insurance, 401(k) plan, and paid time off. Relocation assistance is available to support candidates transitioning to the location.
OVERWATCH is committed to creating a diverse work environment and is proud to be an Equal Opportunity Employer. OVERWATCH considers candidates regardless of race, color, religion, sex, sexual orientation, gender identity, national origin, disability or veteran status.
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 Overwatch Mission Critical, 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.
Overwatch Mission Critical AI Hiring
Overwatch Mission Critical has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Cedar Rapids, IA, 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
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