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About This Role
Director of AI Platforms and Infrastructure
Aimpoint Digital is a market\-leading data, AI, analytics, and operations research advisory and solution engineering firm. We help organizations design, build, and operationalize enterprise\-grade data and AI platforms, decision intelligence solutions, optimization systems, and production AI applications.
We are not a commodity consulting firm. We are a technical and strategic partner to organizations that need to move beyond experimentation and deploy AI into real business workflows with the architecture, governance, reliability, and operating discipline required for production.
As AI adoption accelerates across both our internal business and our client base, Aimpoint Digital is investing in the platforms, governance, and operational capabilities that make enterprise AI secure, scalable, and cost effective. We are seeking a Director of AI Platforms and Infrastructure to lead this effort.
About the Role
As the Director of AI Platforms and Infrastructure, you will own the strategy, administration, and operational excellence of Aimpoint Digital's enterprise AI platforms. This role is responsible for managing our internal ChatGPT, Claude, and related AI environments while helping clients successfully deploy, govern, and operationalize enterprise AI platforms.
This position sits at the intersection of AI platform administration, enterprise architecture, governance, security, enablement, and client delivery. You will partner with internal technology leadership to ensure our consultants have reliable, secure, and well\-governed AI tooling while also supporting Anthropic enterprise AI clients with platform standup, configuration, user enablement, and operational best practices.
You will build and continuously improve our AI capabilities. This includes establishing governance standards, managing licenses and costs, defining operational processes, supporting client implementations, developing reusable deployment playbooks, and serving as the technical bridge between platform vendors, internal stakeholders, and delivery teams.
The right candidate is highly organized, technically credible, operationally disciplined, and energized by building scalable systems. You should understand enterprise AI platforms, SaaS administration, identity and access management, security and governance, and the practical realities of deploying AI responsibly across large organizations.
What You Will Do
- Own administration and day\-to\-day management of Aimpoint Digital's enterprise AI platforms, including ChatGPT Enterprise, Claude Enterprise, and future AI platform investments.
- Develop and maintain governance standards for user provisioning, permissions, workspace configuration, model access, data controls, and security policies.
- Monitor platform utilization, licensing, adoption, and spend while optimizing costs and maximizing business value.
- Partner with IT, Security, Legal, and AI leadership to establish scalable operating procedures for enterprise AI usage.
- Establish and evolve a point of view on critical AI usage topics such as security, cost management, and governance.
- Serve as the technical lead for client AI platform deployments, including environment setup, configuration, governance workshops, enablement, and administrator training.
- Develop reusable implementation playbooks, reference architectures, deployment checklists, and operational documentation.
- Support delivery teams during enterprise AI implementations by providing platform expertise and operational guidance.
- Evaluate new enterprise AI platform capabilities, administrative features, APIs, integrations, and roadmap enhancements.
- Develop internal enablement programs that help consultants effectively and responsibly leverage enterprise AI platforms.
- Establish reporting and operational metrics around adoption, usage, governance, security, platform health, and ROI.
Who We Are Looking For
We are looking for an entrepreneurial technology leader who enjoys building operational excellence around emerging AI platforms. You are equally comfortable administering enterprise AI environments, partnering with security and IT leaders, and working directly with enterprise clients to deploy and operationalize modern AI platforms.
You understand that successful AI adoption requires more than model access, it requires governance, enablement, cost management, thoughtful administration, and repeatable operational processes. You enjoy creating structure where none exists and translating rapidly evolving AI capabilities into reliable enterprise services.
You are credible with technical architects, security teams, executives, consultants, and platform vendors. You can balance strategic thinking with hands\-on execution and are excited to help shape how Aimpoint Digital and our clients operate enterprise AI at scale.
Qualifications
- 5\+ years of experience in enterprise infrastructure, cloud platforms, SaaS administration, AI platform operations, enterprise architecture, consulting, or related technical leadership roles.
- Experience administering enterprise SaaS platforms, identity and access management, governance, licensing, and operational processes.
- Working knowledge of ChatGPT Enterprise, Claude Enterprise / Cowork / Code / Design or comparable enterprise AI platforms.
- Understanding of cost management considerations across GenAI tools and platforms.
- Understanding of enterprise AI governance, security, compliance, model management, and responsible AI practices, and the ability to align GenAI tools to an organization's security posture.
- Experience supporting enterprise software implementations and client\-facing enablement initiatives.
- Strong project management and cross\-functional collaboration skills.
- Excellent communication skills with the ability to translate technical platform concepts into practical business guidance.
- Experience with APIs, integrations, automation, or scripting is preferred.
Location
We are actively seeking candidates for full\-time, remote work within the United States, with priority evaluation provided for candidates based in New York City, Chicago, and Houston/Dallas. Atlanta\-based applicants will have the opportunity to work in our headquarters in Sandy Springs, GA.
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 Aimpoint Digital, 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. Director-level AI roles across all categories have a median of $272,150.
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.
Aimpoint Digital AI Hiring
Aimpoint Digital has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% of all AI roles offer remote work.
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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