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About This Role
Job Overview
I am an entrepreneur and CEO of an education\-focused company looking for an experienced AI practitioner to work directly with me approximately four half\-days per month in the Indianapolis area.
This is not a traditional training position, consulting engagement, or outsourced developer role.
I am looking for someone who can help me rapidly increase my AI expertise by teaching me while we build real solutions together.
My primary goal is to become a significantly more AI\-capable CEO. I want to understand AI deeply enough to identify opportunities, build practical solutions, evaluate technical decisions, work intelligently with developers, and eventually teach educational leaders how they can use AI to operate more effectively.
Our sessions will be highly applied. Rather than spending hours discussing concepts or watching demonstrations, we will identify real business problems and build solutions together.
Initial projects will likely include:
- Internal AI automations that increase operational efficiency within my company.
- Working AI agents that can perform or support multi\-step business processes.
- Simple software applications that solve real problems for our team or customers.
- Prototypes and experiments that may eventually influence larger products we develop.
What I Want to Learn
I am already a frequent AI user. I am not looking for an introduction to ChatGPT or basic prompting.
I want someone who can help me progress from being an advanced AI user into someone who understands how AI systems are actually designed and built.
Areas of learning may include:
- AI Agents
- What agents actually are and when they should be used.
- How agents differ from prompts, workflows, and traditional automation.
- How agents use tools, memory, context, APIs, and external information.
- How to design, build, test, and improve useful agents.
- How to determine when an agent is unnecessarily complicated.
- Workflow and Business Automation
- Identifying repetitive work that should be automated.
- Mapping business processes before automating them.
- Using platforms such as n8n, Make, Zapier, or similar tools.
- Connecting AI to existing business systems.
- Designing human approval and oversight into automated processes.
- Coding and Application Development
- Learning the fundamentals of coding through practical projects.
- Understanding Python, JavaScript, APIs, databases, and other concepts at a useful executive\-builder level.
- Using AI coding tools to accelerate development.
- Building simple internal tools and applications.
- Understanding how modern AI applications move from idea to prototype to production.
- AI Product Development
- Translating a business problem into an AI product concept.
- Understanding architecture decisions without needing to become a senior software engineer.
- Evaluating technology stacks and development approaches.
- Understanding build\-versus\-buy decisions.
- Estimating complexity, development effort, ongoing maintenance, and cost.
- Becoming a much stronger buyer and manager of outside AI development.
- AI Systems and Architecture
- APIs and model integrations.
- Retrieval\-Augmented Generation and knowledge bases.
- Structured and unstructured data.
- Model selection.
- Context management.
- Tool calling.
- Agent architecture.
- Data flow between systems.
- Reliability and Evaluation
- Determining what a good AI output actually looks like.
- Testing agents and AI applications.
- Identifying failure points.
- Creating evaluation criteria.
- Understanding hallucinations and reliability issues.
- Building appropriate guardrails and human oversight.
- AI Strategy
- Identifying where AI can create meaningful leverage in a business.
- Distinguishing valuable AI opportunities from technology hype.
- Evaluating emerging AI tools and platforms.
- Understanding how AI may change organizational structures and workflows.
- Identifying opportunities that I may not currently know enough to recognize.
The Bigger Objective
I lead a company that works extensively with school and district leaders.
One of my longer\-term goals is to translate what I learn into practical ways educational leaders can use AI to improve strategy, communication, productivity, decision\-making, and operational efficiency.
For that reason, I am particularly interested in someone who can help me develop both:
Technical understanding: How AI systems actually work and are built.
Executive understanding: Where AI creates value, what should be automated, what should remain human\-led, and how organizations should think about adopting AI responsibly.
I am also involved in developing AI\-enabled products. Increasing my technical fluency will allow me to make significantly better decisions when working with developers, evaluating product ideas, setting priorities, and determining what should and should not be built.
How We Will Work
This role will be intentionally different from a normal training engagement.
We will generally meet four half\-days per month, in person, in the Indianapolis area.
I am not interested in virtual sessions.
During our time together, I want us sitting beside each other and actually building.
A typical session might involve:
- Selecting a real business problem.
- Mapping how the work currently happens.
- Determining whether the best solution is prompting, automation, a workflow, an agent, or an application.
- Designing the solution together.
- Building the solution together.
- Testing and troubleshooting it.
- Helping me understand why it works.
- Having me reproduce or modify parts of the system myself.
- Discussing how the underlying concept could apply to other business or educational leadership problems.
The objective is not simply for you to build something for me.
The objective is for my capability to increase every month.
Who I Am Looking For
The ideal person is an experienced AI practitioner who has actually built useful AI systems.
You may be an:
- AI engineer
- AI automation consultant
- Software engineer working heavily with AI
- AI product builder
- Technical founder
- Applied AI consultant
- Automation specialist with strong AI expertise
Formal teaching experience is not required.
However, you must be able to explain technical concepts clearly to a highly engaged non\-engineer who wants to understand how things actually work.
Strong Candidates Will Have Experience With Several of the Following
- Building AI agents
- AI APIs
- OpenAI, Anthropic, Gemini, or other major model platforms
- Python and/or JavaScript
- AI\-assisted coding environments
- n8n, Make, Zapier, or similar automation tools
- APIs and webhooks
- Databases
- RAG and knowledge systems
- Agent frameworks
- Application prototyping
- AI evaluation and testing
- AI product architecture
- Deploying practical AI solutions inside businesses
I am less concerned with knowing every specific platform than I am with finding someone who understands the principles underneath the tools.
The Person I Do Not Want
This will probably not be a good fit if your AI expertise primarily consists of:
- Prompt engineering
- ChatGPT training
- Selling AI courses
- Creating demonstrations without deploying real systems
- No\-code automation without understanding the technology underneath it
- Software development without meaningful AI experience
I want someone who builds.
What Success Looks Like
After working together for 6 to 12 months, I should be substantially different as a CEO.
I should be able to:
- Identify valuable AI opportunities inside my business.
- Design basic AI workflows and agents.
- Build or modify simple AI applications.
- Understand enough code to work intelligently with AI\-assisted development.
- Understand APIs, data, models, agents, workflows, and application architecture at a practical level.
- Evaluate whether an AI solution is well designed.
- Ask developers significantly better questions.
- Make stronger decisions about AI product development.
- Recognize unnecessary technical complexity.
- Understand AI well enough to teach practical applications to other organizational leaders.
- Develop prototypes that could become internal systems, client\-facing solutions, or future products.
- Continue learning independently because I understand the underlying concepts rather than simply knowing how to use individual tools.
Compensation
Compensation is flexible and will be based on the level of expertise and value you can bring.
I am willing to pay a premium for the right person.
This is not about finding the least expensive trainer. I am looking for someone whose experience can meaningfully accelerate my learning and help me avoid spending years trying to develop this knowledge on my own.
The expected commitment is approximately four half\-days per month, with the possibility that the relationship evolves as projects and opportunities develop.
Location Requirement
You must be able to regularly meet in person in the Indianapolis, Indiana area.
Please do not apply if you are only available virtually.
Face\-to\-face, hands\-on collaboration is a fundamental part of this position.
How to Apply
Rather than sending only a traditional resume, please include a brief response addressing the following:
- Describe two or three AI agents, automations, or applications you have personally built.
- What parts of those systems did you personally design or develop?
- What AI development tools and platforms do you currently use most?
- Give an example of a business process you believe should be automated and one that you believe should not.
- How would you teach a non\-programmer to begin building AI applications?
- If you had four half\-days per month with a CEO for one year, how would you structure the learning experience?
- What do you believe business leaders misunderstand most about AI right now?
- What would you want me to be capable of doing independently after six months?
- Are you able to consistently meet in person in the Indianapolis area?
- What compensation structure would make this engagement worthwhile for you?
Please include links to projects, GitHub repositories, applications, demos, case studies, or other examples of your work when appropriate.
I am particularly interested in candidates who can look at the goals described above and say, “There are several things you haven't mentioned that you also need to understand.”
Pay: From $2,000\.00 per month
Work Location: Hybrid remote in Indianapolis, IN 46202
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 EES Innovation, 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.
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.
EES Innovation AI Hiring
EES Innovation has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Indianapolis, IN, US.
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.
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