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About This Role
AI Solutions Developer Introduction:
Join our growing team as an AI Solutions Developer, where you will harness your technical expertise and knowledge of AI technologies to drive operational efficiency across our organization. This role is tailored for a professional who is passionate about applying AI agent development, low-code/no-code solutions, and helping users leverage AI in common applications to streamline processes and enhance productivity. The focus is on key business workflows and targeted automation needs. You will collaborate closely with the Business Systems Analyst, the Senior Software Architect, and various teams to augment operational workflows, building solutions that are fast to deploy, well-documented, and aligned with the firm's architectural standards.
The position will report to the Software Development Team supervisor and be based out of the Westwood office. Job Responsibilities:
- Collaborate with the Business Systems Analyst and cross-functional teams to identify operational bottlenecks and areas for automation.
- Develop and implement low-code/no-code solutions to automate processes, ensuring efficiency and accuracy in data handling and reporting.
- Leverage AI tools and platforms to create agent workflows that optimize operational tasks and enhance team productivity.
- Partner with teams across the firm to help them better leverage AI capabilities built into existing applications, identifying opportunities where built-in features can address needs without custom development.
- Design and deploy AI agent workflows within the firm's established architectural standards, collaborating with the senior software architect and participating in solution reviews.
- Maintain clear documentation for automation solutions and agent configurations to ensure long-term supportability.
- Apply development best practices to low-code/no-code solutions, including peer review where appropriate.
- Develop metrics to measure the impact of automation solutions on operational efficiency and report findings to stakeholders.
- Stay current with emerging AI technologies and methodologies, integrating them into operational practices to drive continuous improvement.
- Connect AI tools and automation solutions to existing business systems through integrations.
- Contribute to the development team's growing expertise in AI coding practices and tools through collaboration and knowledge sharing.
- Ensure compliance with the AI Acceptable Use Policy, promoting responsible and ethical use of AI technologies in all solutions developed.
Job Requirements:
- 2-4 years of relevant experience in software development, with proficiency in AI coding and automation.
- Working proficiency in Python and/or TypeScript, with experience building scripts and workflows for AI agent development.
- Demonstrated experience with low-code/no-code platforms and AI tools (e.g., Microsoft Power Platform, Pega, GitHub Copilot, Claude Code, Codex, Cursor).
- Ability to assess business needs and recommend the right-sized solution, whether that's leveraging built-in AI features, configuring a low-code workflow, or developing a custom script.
- Experience with API integrations and connecting tools to existing business systems.
- Working knowledge of data management practices, including data extraction, validation, and transformation.
- Familiarity with software development lifecycle principles, including basic architectural patterns, code review, and change management.
- Ability to articulate how automation solutions fit within a broader systems architecture.
- Strong analytical skills with the ability to solve complex problems and develop practical solutions.
- Ability to translate business needs into practical automation solutions.
- Ability to evaluate AI output quality and reliability
- Excellent collaboration skills, with experience working in cross-functional teams.
- A proactive mindset with a passion for experimenting with new technologies and approaches.
About Meketa Investment Group
Meketa Investment Group is an employee-owned full-service investment consulting and advisory firm. We work with some of the nation’s largest and most sophisticated institutional investors including public and private plan sponsors. We have a staff of over 250 employees among our six US office locations and London.
Meketa recognizes that our workforce is a reflection of our company, and we operate in an inclusive environment that accepts and promotes diversity. We believe our employees can best serve our clients in an atmosphere where individuals are treated fairly, where professional growth is developed and encouraged, and where a healthy balance between work and home life is respected and preserved. To support employees, Meketa offers a competitive compensation structure, a wide range of benefits from core insurance benefits to time-based benefits, as well as flexible schedules and support for continuing education.
Meketa is an Equal Employment Opportunity and Affirmative Action Employer.
If reasonable accommodation is needed to participate in the job application or interview process, to perform essential job functions, and/or to receive other benefits and privileges of employment, please contact us.
Salary Context
This $85K-$100K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Meketa Investment Group, 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($92K) sits 56% below the category median. Disclosed range: $85K to $100K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Meketa Investment Group AI Hiring
Meketa Investment Group has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Westwood, MA, US. Compensation range: $100K - $100K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 Safety roles lead at $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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