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About This Role
Benefits:
- Competitive salary
- Flexible schedule
- Training \& development
ABOUT THE ROLE
Citadel Development Services is a fast\-growing real estate development and construction firm operating across multiple service lines — Due Diligence, Design, Estimating, Construction, and Finance. We are building an AI\-powered operation from the inside out, and we need an internal AI Engineer to make it happen.
This is not a research role. This is a build\-and\-deploy role. You will be embedded with our teams to identify operational bottlenecks, design AI agent solutions, and ship working automations that save time, eliminate errors, and help us scale.
You will work directly with leadership and department heads to develop agents across platforms such as OpenAI (custom GPTs), Claude, Make.com, Zapier, and similar no\-code/low\-code AI orchestration tools — translating our strategy into real, measurable automation wins.
THE MISSION — WHY THIS ROLE EXISTS
CDS's 2026 strategy explicitly calls out AI as a core lever across multiple departments:
- PH1/PH2: Implement AI to analyze municipalities, review reports, flag areas of concern, and organize permitting information.
- Estimating: Use AI to improve accuracy, minimize scope gaps, and eliminate errors in bids
- HR / Recruiting: Build an AI workflow to screen and qualify candidates at scale using a scoring system
- Finance: Automate vendor/supplier AP tracking and cash flow modeling
- Business Development: Support outreach workflows, portfolio updates, and client onboarding processes
You will be the person who builds, deploys, and maintains all of it.
KEY RESPONSIBILITIES
Agent Design \& Deployment
- Design, build, and deploy AI agents tailored to specific departmental workflows across CDS
- Develop and maintain custom GPTs, Claude projects, and similar LLM\-based tools using platforms such as OpenAI, Anthropic, and Cohere
- Architect multi\-step automation workflows using Make.com, Zapier, n8n, or similar orchestration tools
- Build intake forms, triggers, and conditional logic to route information to the right agent at the right time
Cross\-Department Implementation
- Collaborate directly with PH1/PH2 (Due Diligence \& Permitting) to build AI pipelines for municipality research and report analysis
- Partner with Estimating to develop AI\-assisted bid review, scope gap detection, and subcontractor qualification tools
- Work with HR to implement resume screening AI workflows, scoring rubrics, and candidate ranking systems
- Support Finance with automated AP tracking, cash flow dashboards, and expense anomaly detection
- Assist Business Development with outreach sequencing, portfolio maintenance automation, and CRM\-connected workflows
Systems Integration
- Connect AI agents to CDS's existing tools — Procore, Smartsheet, Harvest, and others — via APIs and webhooks
- Integrate with Google Workspace (Gmail, Drive, Calendar) and communication platforms (Slack, email) as needed
- Ensure data flows cleanly between systems with appropriate error handling and logging
Documentation \& Training
- Document all agents, workflows, and automations clearly so the team can understand and maintain them
- Train department leads and team members on how to use and prompt AI tools effectively
- Build a library of prompt templates and SOPs tailored to CDS workflows
Continuous Improvement
- Monitor agent performance, identify failure points, and iterate rapidly
- Stay current on emerging AI tools, models, and platforms — bring recommendations to leadership
- Measure time saved, errors reduced, and output quality improved across deployed agents
REQUIRED SKILLS \& CAPABILITIES
\| AI Platforms \| OpenAI (GPT\-4, custom GPTs, Assistants API), Anthropic Claude, Gemini; ability to build, configure, and deploy agents on each
\| Prompt Engineering \| Expert\-level prompt design for instruction\-following, structured output, classification, extraction, summarization, and multi\-step reasoning tasks
\| Automation Tools \| Make.com (Integromat), Zapier, n8n — building multi\-step workflows with branching logic, error handling, and data transformation
\| APIs \& Webhooks \| REST API integration, JSON/XML parsing, OAuth, webhook configuration; ability to connect AI agents to third\-party platforms without native connectors
\| Programming \| Python (required): scripting, data processing, API calls, file manipulation. JavaScript a strong plus. Ability to write clean, functional scripts — not production engineering, but reliable automation code.
\| Data Handling \| Spreadsheet logic (Excel/Google Sheets), basic SQL or structured data querying, JSON/CSV manipulation, data cleaning and transformation
\| AI Agent Frameworks \| Familiarity with LangChain, CrewAI, AutoGen, or similar multi\-agent orchestration frameworks; understanding of RAG (Retrieval\-Augmented Generation) architectures
\| Tool Integration \| Google Workspace (Gmail, Drive, Docs, Sheets), Slack, Procore, Smartsheet, Harvest, or similar PM/construction platforms
\| Vector Databases \| Basic understanding of embeddings, vector search (Pinecone, Weaviate, Chroma), and knowledge base retrieval for document\-aware AI agents
\| Problem Solving \| Ability to sit with a department head for 30 minutes, map their workflow, identify where AI creates leverage, and propose a build plan the same day
Salary Context
This $90K-$110K range is above the median for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Citadel Development Services LLC, 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 $166,983 based on 13,781 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $131,300. This role's midpoint ($100K) sits 40% below the category median. Disclosed range: $90K to $110K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Citadel Development Services LLC AI Hiring
Citadel Development Services LLC has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Austin, TX, US. Compensation range: $110K - $110K.
Location Context
AI roles in Austin pay a median of $212,800 across 317 tracked positions. That's 16% above the national 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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