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About This Role
Associate AI Solutions Engineer Founded in 1997, Think Together partners with schools to change the odds for kids by providing award-winning programs during and outside the school day. We’re one of California’s largest nonprofits working in school districts from San Diego to San Francisco. Whether you’re interested in early learning, elementary, middle or high school, you can invest in your community by working at a local school or working within our home office providing support! JOB SUMMARY
Reporting to the Leader, AI Transformation & Strategic Projects, the AI Solutions Engineer advances promising AI Champion discoveries from validated concept into secure, functional, supportable solutions that can scale across Think Education and Think Together.
Working under general direction, this role builds, integrates, tests, deploys, monitors, and supports AI agents, copilots, retrieval solutions, and workflow automations using approved enterprise platforms and established architecture, security, privacy, accessibility, and responsible AI practices.
The role partners with the AI Business Process & Automation Analyst, AI Champions, cybersecurity, architecture, platform owners, service teams, and business process owners to translate approved requirements into reliable technical outcomes. It creates the documentation, evaluation evidence, operational controls, and knowledge transfer needed for safe production use.
ESSENTIAL JOB DUTIES AND RESPONSIBILITIES:
Agentic AI Development* Design and deploy scalable, production-ready AI agents using approved enterprise platforms, architectures, and development standards.
- Develop and evaluate permission-aware Retrieval Augmented Generation (RAG) solutions using organizational knowledge repositories.
- Integrate AI capabilities with enterprise systems and platforms (e.g., Teams, CRM) to ensure secure data access and AI-enabled business processes.
- Apply context-engineering and harness engineering practices to improve solution quality, reliability and maintainability.
- Build the orchestration, evaluation, guardrails, observability, and feedback mechanisms required to operate AI solutions safely at scale.
- Develop and implement AI testing frameworks that access model quality, security, safety, reliability, performance, accessibility, and cost across the solution lifecycle.
- Document evaluation results, residual risks, acceptance decisions, and human oversight requirements before release to inform decisions and ensure AI solutions are ready for safe and responsible deployment.
AI Platform Engineering* Configure and maintain AI platforms, access controls, and governance mechanisms to ensure secure, compliant, and scalable AI operations.
- Partner with cybersecurity and enterprise architecture teams to shape AI solution designs.
- Operationalize applicable AI governance standards by translating them into practical requirements and controls that enable responsible and scalable AI solutions.
- Implement environment separation, source control, peer review, automated testing, deployment approvals, versioning, release documentation, and rollback capabilities.
- Manage monitoring for solution quality, availability, latency, consumption, cost, security events, model or data drift, and control failures; identify issues and drive timely remediation.
- Establish governed knowledge-ingestion and retrieval practices covering authoritative sources, ownership, permissions, freshness, retention, deletion, and lineage.
- Manage AI incidents, problems, vulnerabilities, vendor and model changes, and corrective actions in coordination with cybersecurity and service-management teams.
- Define continuity, recovery, fallback, and graceful-degradation approaches for business-critical AI capabilities.
Solution Delivery* Own the technical path from feasibility assessment through engineering, production readiness, release, and operation, partnering with the AI Business Process & Automation Analyst.
- Conduct documented technical feasibility, architecture, effort, dependency, operatingcost, and risk assessments before pilots or production commitments are approved.
- Document technical architectures and operating procedures.
- Provide or coordinate Tier 3 support for AI solutions.
Knowledge Transfer* Mentor AI Champions and Technologists.
- Provide technical training to business analysts and solution owners.
- Establish reusable design patterns and development standards.
This position includes both “build” and “run” IT service duties. Build duties are the addition of new systems, features, and functions for both continuous improvement and to support Think Education’s growth. Run duties are the on-going support of the Think Education systems, applications, data environments, and processes to ensure they are meeting users’ needs. The amount of effort dedicated to build and run duties will fluctuate from time to time depending on the needs of the business.
QUALIFICATIONS AND REQUIREMENTS:
- Bachelor’s degree in computer science, software engineering, information systems, data science, or a related field preferred, or an equivalent combination of relevant experience, certifications, coursework, and demonstrated applied learning.
- Two or more years of relevant experience in software development, automation, data, integration, application support, cloud platforms, or a related technical discipline, including experience contributing to the delivery or support of production systems. Relevant internal experience and a demonstrated portfolio may substitute for direct AI job experience.
- Demonstrated ability to build or configure at least one working automation, integration, agent, copilot, retrieval solution, or similar technical solution; connect it to an Application Programming Interface (API), data source, or enterprise system; and explain how it was tested, deployed, monitored, and supported.
- Working knowledge of generative AI concepts, prompt design, retrieval augmented generation, agent workflows, model limitations, human oversight, and at least one scripting or programming language such as Python, JavaScript, TypeScript, PowerShell, or SQL, together with source control, structured testing, release and change controls, monitoring, troubleshooting, security, privacy, accessibility, records, and responsible AI practices.
PREFERRED QUALIFICATIONS
- Experience with Microsoft 365, SharePoint, Teams, CRM, service management, or related enterprise platforms.
- Experience advancing validated pilots into production-ready solutions.
- Experience with cloud-based AI services, Continuous Integration/Continuous Delivery, observability, and release management.
- Experience building workflows and/or agentic solutions.
- Experience mentoring and supporting non-technical practitioners.
WORK ENVIRONMENT
- Moderate noise, such as typical office noise.
- Ability to stand, walk, and sit in a general office environment.
- Regular computer screen use in the performance of the role.
- Ability to lift up to 10 pounds.
- Travel up to 10 to 20 percent.
- Must pass Live Scan (criminal background check via fingerprinting).
- Provide and maintain a clear TB test throughout employment.
- Reliable transportation is required to meet the needs of the position.
- While driving may not be a regular requirement, employees who operate a personal or company vehicle related to work location requirements must maintain, and provide Human Resources, proof of a valid driver’s license and automobile insurance.
COMPENSATION:
SALARY - $95,000-105,000
This is the targeted compensation for the position. A range of factors, including but not limited to; location, skills, experience, will be considered. Actual compensation may vary.
Think Together is an Equal Opportunity Employer that does not discriminate on the basis of actual or perceived race, color, national origin, ancestry, sex, gender, gender identity, gender expression, pregnancy, childbirth or related medical conditions, religious creed, physical disability, mental disability, age for individuals age 40 and over, medical condition (as defined by state law (for example, cancer or genetic characteristics or HIV/AIDS), marital status, military and veteran status, sexual orientation, genetic information, citizenship status or any other characteristic protected by federal, state or local law. Our management team is dedicated to this policy with respect to recruitment, hiring, placement, promotion, transfer, training, compensation, benefits, employee activities and general treatment during employment.
Salary Context
This $95K-$105K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Think Together, 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 $212,000 based on 11,866 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $110,408. This role's midpoint ($100K) sits 53% below the category median. Disclosed range: $95K to $105K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Think Together AI Hiring
Think Together has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Santa Ana, CA, US. Compensation range: $105K - $105K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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