Interested in this AI/ML Engineer role at Idealist Consulting?
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About This Role
Are you equal parts data driven and relationship driven with a good sense of humor and an altruistic streak? You might be an Idealist!
At Idealist Consulting, we are innovators and problem\-solvers committed to global progressive action. Our expertise spans Salesforce implementation, managed services, and the full AI adoption journey, from strategic planning and data readiness to integration, agent configuration, and user adoption. Idealist Consulting has been B Corp Certified since 2008\.
We’re seeking an AI Solution Architect to guide the expansion of our AI for Nonprofits service offerings. Ideal candidates will have an ear to ground in the latest AI and Agentic solutions that support nonprofit business processes.
### How You’ll Contribute:
- Guide the development and quality of Idealist Consulting’s AI for Nonprofits services through the selection of platforms, products, and integrated applications for solution implementation through:
- Research, evaluate, and report back on new AI products, releases, and agentic capabilities
- Steer scoping and other pre\-sales processes as Solution Architect
- Serve as Idealist Consulting’s primary delivery contact with the Anthropic Partnership contacts and advise on joint go\-to\-market initiatives and co\-development of nonprofit use cases
- Define and refine best practices within the AI for Nonprofits industry specialization
- Collaborate with Director of Service Delivery on AI implementation methodology and process documentation in the Idealist Way
- Champion responsible AI principles including transparency, data privacy, bias mitigation, and ethical use — particularly in high\-stakes nonprofit contexts such as casework, advocacy, and beneficiary data
- Develop standards for AI proof\-of\-concept (POC) and minimum viable product (MVP) evaluation in collaboration with clients and internal teams
- Provide mentorship for Consultants within the AI for Nonprofits industry specialization
- Contribute to performance reviews, talent development, and training efforts
- Contribute to monthly Consultant meetings
- Assure service quality through project audits and common practice assessments
- Contribute to industry thought leadership and innovation
- Demonstrate thought leadership and contribute content including blogs, capabilities content, customer stories, and sessions
- Represent the company at industry events
- Lead project teams and Solution Architecture in the Idealist Way on assigned projects (50% utilization)
- Translate client business requirements into well\-architected AI solutions that best leverage the Claude platform and integrated applications
- Assist Sales and Services Teams in preparing estimates for implementation, providing detailed level\-of\-effort estimates for proposed solutions as needed
- Guide project teams in the Idealist Way throughout the implementation cycle, including discovery, use case identification, prompt engineering, agent configuration, quality assurance, and user acceptance testing
### What You Bring to the Table:
*Baseline Expectation:*
- Excellent client\-facing written and oral communications skills
- Proven ability to design and optimize business processes and integrate AI capabilities across disparate systems
- Minimum 3 years experience designing and deploying generative AI or LLM\-based solutions in a consulting or enterprise context
- Minimum 3 years nonprofit sector experience, with deep familiarity with common nonprofit business processes: fundraising, case management, program delivery, advocacy, and volunteer management
- Minimum 2 years experience using story mapping methodology to capture business requirements
- Minimum 3 years leading technology implementation project teams
- Minimum 3 years experience utilizing Agile and Waterfall project management methodology
- Desire to learn new things and take on challenges
- Subject Matter Expertise in Nonprofit Solutioning
- Platform expertise in AI tools, LLM\-based products, and agentic workflow configuration
- Exceptional communication and presentation skills
*Extra Credit:*
- 2 years\+ working in the Idealist Way or equivalent delivery methodology
- Direct experience with Anthropic’s Claude, the Claude API, or enterprise AI platforms (AWS Bedrock, Azure OpenAI, Google Vertex AI)
- Proven track record of delivery Anthropic’s Claude\-specific solutions
- Salesforce Nonprofit Cloud Consultant certification or equivalent nonprofit CRM expertise
- Experience building or managing an AI practice, center of excellence, or emerging technology offering at a consulting firm
- PMP Certification or similar
- Published thought leadership, conference presentations, or community contributions in AI, nonprofit technology, or responsible tech
### What You Can Expect:
- Equitable total compensation package with base salary commensurate with experience plus performance bonuses
- Standardized growth targets at 90 days, 1 year, and beyond
- Humane, no\-overtime work weeks and a Hybrid Work Policy with remote options and flexible workday scheduling
- Healthcare coverage options with HSA/FSA contribution match
- Generous Paid Time Off and Family Leave policies
- Paid Volunteer Time to encourage civic engagement and community involvement
- 401k employer match and short\-term leave available with tenure
$135k\-$160K OTE including:
Base salary
Monthly utilization bonus
Dedicated education and training funds
Home office reimbursement
Idealist Consulting was founded in 2006 by returning Peace Corps volunteer Rob Jordan to help nonprofits and progressive organizations grow mission impact through technology. We’re committed to delivering innovative technical solutions to empower organizations working on some of the world's most important causes. *Idealist Consulting is an equal opportunity employer. Idealist Consulting does not discriminate against individuals on the basis of race, color, gender, sexual orientation, gender identity, religion, disability, age, genetic information, veteran status, ancestry, or national or ethnic origin in the administration of its consulting and employment policies. We have a majority female leadership team and believe in the B Corp Declaration of Interdependence which states that businesses should aspire to do no harm and benefit all. Read our Commitment to Diversity, Equity, and Inclusion on our Ethics page.*
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
Salary Context
This $135K-$160K 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 Idealist Consulting, 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 ($147K) sits 12% below the category median. Disclosed range: $135K to $160K.
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.
Idealist Consulting AI Hiring
Idealist Consulting has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Chicago, IL, US. Compensation range: $160K - $160K.
Location Context
AI roles in Chicago pay a median of $202,350 across 310 tracked positions. That's 10% 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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