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About This Role
Founded in 2012, H2O.ai is on a mission to democratize AI. As the world’s leading agentic AI company, H2O.ai converges Generative and Predictive AI to help enterprises and public sector agencies develop purpose\-built GenAI applications on their private data. With a focus on Sovereign AI—secure, compliant, and infrastructure\-flexible deployments—H2O.ai delivers solutions that align with the highest standards of data privacy and control.
Our open\-source technology is trusted by over 20,000 organizations worldwide, including more than half of the Fortune 500\. H2O.ai powers AI transformation for companies like AT\&T, Commonwealth Bank of Australia, Chipotle, Workday, Progressive Insurance, and NIH.
H2O.ai partners include NVIDIA, Dell Technologies, Deloitte, Ernst \& Young (EY), Snowflake, AWS, Google Cloud Platform (GCP), VAST Data and MinIO. H2O.ai’s AI for Good program supports nonprofit groups, foundations, and communities in advancing education, healthcare, and environmental conservation. With a vibrant community of 2 million data scientists worldwide, H2O.ai aims to co\-create valuable AI applications for all users.
H2O.ai has raised 256 million from investors, including Commonwealth Bank, NVIDIA, Goldman Sachs, Wells Fargo, Capital One, Nexus Ventures and New York Life.
For more information, visit www.h2o.ai.
About This Opportunity
We are looking for a Senior AI Engineer who builds things that matter. You will design and ship end\-to\-end AI solutions for some of APAC's most complex enterprise problems \- spanning agentic AI systems, LLM applications, and production ML pipelines. This is a hands\-on engineering role embedded within a customer\-facing field team, meaning your work will be seen, used, and evaluated by real enterprises from day one.
You will work alongside Kaggle Grandmasters, ML engineers, and domain experts to deliver AI that goes beyond demos \- into production, into workflows, and into measurable business outcomes.
This position is based in Chicago, Illinois.
What You Will Do
*Agentic AI \& LLM Engineering*
- Design and build agentic AI systems and multi\-agent frameworks that automate complex, multi\-step workflows for enterprise customers.
- Develop and deploy LLM\-powered applications using techniques including RAG, fine\-tuning, prompt engineering, function calling, and tool use.
- Implement guardrails, evaluation frameworks, and responsible AI controls to ensure production\-grade reliability and safety.
- Stay current with the rapidly evolving agentic AI landscape \- MCP, LLM orchestration frameworks, reasoning models \- and bring the best of it into customer engagements.
*End\-to\-End AI Application Development*
- Own the full development lifecycle: from problem framing and data exploration through model development, API integration, and production deployment.
- Build scalable backend services and APIs that expose AI capabilities to enterprise applications and workflows.
- Integrate AI models into customer environments \- cloud, on\-prem, and hybrid \- ensuring performance, stability, and maintainability at scale.
- Develop ML pipelines and LLMOps infrastructure that support continuous model improvement and monitoring in production.
*Customer Engagement \& Delivery*
- Work directly with customer data scientists, engineers, and business stakeholders to translate real\-world problems into AI solutions.
- Contribute to pre\-sales and proof\-of\-concept engagements \- building fast, credible demonstrations that win technical trust.
- Communicate clearly across audiences: from detailed technical design reviews with engineering teams to outcome\-focused updates for business stakeholders.
- Collaborate closely with Program Managers, Solution Engineers, and Kaggle Grandmasters to deliver cohesive, high\-quality solutions.
What We Are Looking For
*Experience \& Background*
- 3\+ years of hands\-on AI/ML engineering experience, including end\-to\-end model development and production deployment.
- Demonstrable experience building LLM\-powered applications \- RAG pipelines, agentic workflows, fine\-tuned models, or similar.
- Strong Python engineering skills; experience with ML frameworks (PyTorch, TensorFlow, scikit\-learn) and LLM tooling (LangChain, LlamaIndex, or equivalent).
- Experience deploying models and AI services in cloud or enterprise environments (AWS, Azure, GCP, on\-prem Kubernetes).
*Skills \& Capabilities*
- Deep understanding of modern GenAI concepts: prompt engineering, RAG, fine\-tuning, RLHF, model evaluation, guardrails, and LLMOps.
- Solid grounding in classical ML \- able to select the right tool for the problem, not just default to the latest LLM.
- Backend development skills: REST APIs, containerization (Docker/Kubernetes), and CI/CD pipelines for AI applications.
- Strong problem\-solving instincts \- comfortable with ambiguity, able to move fast without sacrificing engineering quality.
- Clear communicator who can explain complex AI systems to non\-technical stakeholders without oversimplifying.
How to Stand Out From the Crowd
- Kaggle or competitive ML experience.
- Familiarity with H2O.ai products, Wave, or H2O Document AI.
- Experience in financial services, healthcare, or other regulated industry AI deployments.
- Exposure to tabular foundation models, AutoML, or enterprise ML platforms.
- Prior experience in a customer\-facing or field engineering role.
Why H2O.ai?
- Market leader in total rewards
- Remote\-friendly culture
- Flexible working environment
- Be part of a world\-class team
- Career growth
H2O.ai is committed to creating a diverse and inclusive culture. All qualified applicants will receive consideration for employment without regard to their race, ethnicity, religion, gender, sexual orientation, age, disability status or any other legally protected basis.
H2O.ai is an innovative AI cloud platform company, leading the mission to democratize AI for everyone. Thousands of organizations from all over the world have used our cutting\-edge technology across a variety of industries. We’ve made it easy for people at all levels to generate breakthrough solutions to complex business problems and advance the discovery of new ideas and revenue streams. We push the boundaries of what is possible with artificial intelligence.
H2O.ai employs the world’s top Kaggle Grandmasters, the community of best\-in\-the\-world machine learning practitioners and data scientists. A strong AI for Good ethos and responsible AI drive the company’s purpose.
Please visit www.H2O.ai to learn more.
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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 H2O.ai, 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. Senior-level AI roles across all categories have a median of $227,400.
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.
H2O.ai AI Hiring
H2O.ai has 5 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span Charlotte, NC, US, Chicago, IL, US, US.
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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