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About This Role
AI Platform Engineer
HED is hiring an AI Platform Engineer to develop, evaluate, and improve the AI capabilities that support our people and our practice.
About HED
We are a team that is full of ideas, experience, creativity, passionate opinions, insatiable curiosity, uncompromising integrity, commitment, and skill. Our culture is about aspiration, embracing change and challenges, listening to (and learning from) each other, encouraging continual learning, and inspiring collective growth. As an inclusive, integrated architecture and engineering practice, we value the diversity of perspectives, experiences, abilities, and expertise that advance both the work we do, and the world we share.
Position Summary
At HED, we believe the most powerful solutions emerge when expertise, technology, and human insight come together. As a national integrated architecture and engineering firm, we are advancing how artificial intelligence connects knowledge, improves workflows, and empowers our people to deliver exceptional outcomes.
You will build and optimize production AI solutions, evaluate large language models, improve AI-generated responses, and transform organizational knowledge into secure, accessible, and actionable intelligence.
Working within our Data & AI team, you will own defined technical initiatives and collaborate with developers, data engineers, and business stakeholders. This role offers opportunities to influence technical decisions and grow into broader platform leadership.
Essential Functions
AI Model Evaluation and Performance
- Develop and maintain model benchmarking, evaluation datasets, testing methods, and performance baselines across production AI applications.
- Evaluate models, prompts, and configurations against response quality, reliability, cost, latency, and risk; document findings and recommend improvements.
- Support preproduction validation of model, prompt, and application changes.
Context Engineering and AI Optimization
- Develop and optimize retrieval-augmented generation (RAG), retrieval ranking, prompt structures, context management, and caching.
- Measure answer quality, token usage, and operating costs; use results to guide improvements.
- Collaborate with application developers to improve shared AI capabilities and user experiences.
AI Solution Development and Delivery
- Deliver defined AI use cases from requirements and design through implementation, testing, documentation, adoption, and improvement.
- Help design workflows that capture and organize employee expertise, skills, and competencies for use in AI-powered applications.
- Develop structured data models, skills taxonomies, and knowledge capture workflows; partner with data engineering to integrate governed datasets.
- Work with business stakeholders to define requirements, success criteria, and practical solutions.
Data Governance and Responsible AI
- Apply privacy, security, data classification, consent, retention, and access-control requirements to AI solutions and datasets.
- Support dataset quality, lineage, ownership, documentation, and auditability.
Technical Collaboration and Innovation
- Contribute to architecture discussions, technical reviews, coding standards, automated testing, CI/CD, and code reviews.
- Use AI-assisted development tools responsibly and share technical knowledge with colleagues.
- Evaluate emerging generative AI and agentic technologies for practical application at HED.
Requirements
- Approximately 4–6+ years of experience in software engineering, machine learning engineering, data engineering, or a related technical discipline.
- Hands-on experience developing, deploying, or supporting LLM applications in production.
- Experience implementing AI evaluations, benchmarks, or performance testing.
- Working knowledge of RAG, information retrieval, prompt engineering, and context optimization.
- Proficiency in Python and SQL, Git, automated testing, and modern software development practices.
- Experience with cloud applications, APIs, and data integration workflows.
- Understanding of enterprise data governance, privacy, and security.
- Ability to independently deliver complex technical assignments and communicate effectively with technical and nontechnical stakeholders.
Preferred Qualifications
- Bachelor's degree in Computer Science, Software Engineering, Data Science, Information Systems, or a related field, or equivalent practical experience.
- Experience with Azure OpenAI, Amazon Bedrock, Anthropic, or comparable AI platforms.
- Familiarity with Databricks, Delta Lake, Unity Catalog, MLflow, or similar platforms.
- Experience with AI agents, automated workflows, experiment tracking, or multi-model systems.
Physical Requirements
- Prolonged periods of sitting at a desk and working on a computer.
- Must be able to effectively communicate in writing and verbally via phone, video conferencing, and in person.
- Visual acuity to perform responsibilities.
Work Environment
We embrace a hybrid model that promotes both autonomy and collaboration, including the freedom to work from home, with regular in-office days to connect with teammates and build culture.
The office is a professional, open-space environment designed for collaborative and independent work.
Other Duties
Please note this job description is not designed to cover or contain a comprehensive listing of activities, duties, or responsibilities that are required of the employee for this job. Duties, responsibilities, and activities may change at any time with or without notice.
Salary Context
This $120K-$170K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 2239 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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Harley Ellis Devereaux, 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 $212,040 based on 13,780 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($145K) sits 32% below the category median. Disclosed range: $120K to $170K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Harley Ellis Devereaux AI Hiring
Harley Ellis Devereaux has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Addison, TX, US. Compensation range: $170K - $170K.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, 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 $266,050 median, while Prompt Engineer roles sit at $140,000. 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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.
Frequently Asked Questions
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