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About This Role
Company Overview:
Aust Capital Ventures, LLC is pioneering the next generation of multi\-dimensional technology ecosystems. We are actively developing the AUSI Minimum Viable Product (MVP), a groundbreaking infrastructure system that seamlessly integrates Artificial Intelligence, Machine Learning, Virtual Reality, the Internet\-of\-Things (IoT), and Blockchain. Inspired directly by the human neurological ecosystem, AUSI orchestrates specialized data streams across interconnected modules to optimize operational efficiency and deliver tangible and intangible support resources to our stakeholders.
Role Summary:
We are seeking a visionary Lead AI Infrastructure \& Systems Architect to build, integrate, and deploy the core technical framework of the AUSI ecosystem. In this role, you will be responsible for translating our Project Charter into a functional MVP. You will design and code the systems that connect the three foundational components of the AUSI neurological infrastructure: the AI Mind, the AI Heart, and the AI Gut. This role sits at the intersection of complex multi\-agent orchestration, hardware/software connectivity, enterprise\-grade data validation, and strict regulatory\-driven ethical engineering.
Core Responsibilities:
- Neurological System Implementation: Program the core communication pathways (neurons/neurotransmitters) that route information seamlessly between the Mind, Heart, and Gut ecosystems.
- Engine the AI Mind: Build ingestion pipelines and validation layers for massive datasets, including academic journals, textbook publishers, Library of Congress files, legal codes, and jurisprudential data.
- Engine the AI Heart: Contextualize data outputs against universal human values, moral/ethical behaviors, and emotional, social, and cultural audio/voice support ecosystems.
- Engine the AI Gut: Develop data\-driven telemetry layers integrating live ML models, VR environments, blockchain logs, and comprehensive IoT network connections.
- Build the No\-Code Sandbox: Architect an isolated, high\-connectivity sandbox environment utilizing APIs, Wi\-Fi, Cellular (4G/5G), Bluetooth, LPWAN (LoRaWAN), Zigbee, Satellite, and Fiber\-optics to automate CAD layouts, video/audio rendering studios, and scientific lab telemetry.
- Agent Integration \& Prompt Engineering: Configure external connectors and internal prompting systems to deploy specialized AI Agent Teams modeled after top\-tier professional certifications (e.g., PMP, CFA, CPA, CISA, CompTIA) (pp. 1, 4\-5\).
- Platform Migration Support: Devise strategies to create user preference for the proprietary AUSI AI platform over traditional GitHub platforms.
Regulatory Boundaries \& Grant\-Compliance Guardrails:
A primary, non\-negotiable pillar of this role is engineering the system's operational boundaries. These boundaries are mandatory to satisfy legal frameworks, fulfill governmental regulations, and secure and maintain crucial institutional funding and grants. You will design and implement:
- Grant \& Regulatory Compliance Locking: Hardcode architectural constraints that satisfy data sovereignty, security, and algorithmic accountability mandates required to guarantee grant compliance and maintain ecosystem eligibility.
- Closed\-Domain Boundaries: Architect strict Retrieval\-Augmented Generation (RAG) guardrails ensuring the AI operates purely as a closed system. The ecosystem must explicitly reject external generic queries or unauthenticated data sources to completely prevent hallucinations and intellectual property drift.
- Semantic Guardrails \& Filtering: Build deterministic semantic filters and restriction logic that actively block the system from discussing off\-brand, unverified, out\-of\-scope, or highly sensitive topics that could jeopardize regulatory standup.
- Source Authentication \& Traceability: Implement robust data verification and lineage protocols to guarantee that processing across the AI Mind relies strictly on authorized legal, academic, and administrative source materials, leaving an auditable paper trail.
- Ethical Behavioral Alignment: Code systemic boundaries into the AI Heart that evaluate context and enforce universal human values, ensuring 100% compliance with moral, ethical, and stakeholder\-aligned funding parameters.
Required Technical Skills \& Qualifications:
- Languages \& Frameworks: Elite proficiency in Python, C\+\+, Go, or Rust for low\-latency network orchestration.
- AI \& Agent Orchestration: Extensive experience with LLM frameworks (LangChain, LlamaIndex), RAG architectures, and complex multi\-agent validation loops.
- Compliance \& Guardrail Engineering: Proven track rate implementing deterministic safety frameworks, semantic classification guardrails, and automated input/output filtering (e.g., NeMo Guardrails, LlamaGuard, or custom policy engines).
- System Boundaries \& Auditing: Practical understanding of programming boundaries, input/output constraints, and data\-logging to build a system ready for external regulatory oversight.
- IoT \& Connectivity Protocols: Practical experience routing data across cellular hardware, Bluetooth, LoRaWAN, Zigbee, and real\-time streaming APIs.
- Web3 \& Systems Security: Experience deploying blockchain\-backed verification mechanisms, internal control systems, and data auditing protocols.
Project Scope \& Deliverables:
- Immediate Focus: Delivery of the standalone, fully compliant, and regulatory\-locked AUSI MVP package.
- Key Milestones: Moving rapidly from the initial Design Phase into complete Implementation, system integration, and final platform closure/handover.
- Reporting Structure: You will collaborate directly with our Subject Matter Experts (SMEs) and report directly to the Project Manager and Project Sponsor, Dr. David F. Aust I.
Pay: $160,000\.00 \- $220,000\.00 per year
Benefits:
- Flexible schedule
Work Location: Remote
Salary Context
This $160K-$220K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At AUSI, 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($190K) sits 5% above the category median. Disclosed range: $160K to $220K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
AUSI AI Hiring
AUSI has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $220K - $220K.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,926 positions. About 15% 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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