Interested in this AI/ML Engineer role at HyerTek Inc.?
Apply Now →Skills & Technologies
About This Role
Description:
HyerTek is seeking an experienced and passionate AI Developer to provide technical direction and leadership in the architecture, design, development, and delivery of modern AI solutions for our government clients. This role centers on building production\-ready, AI\-focused applications that extend Microsoft solutions, leverage frontier models via Microsoft Foundry, and align with the Department of War's enterprise generative AI platform, GenAI.mil. Our AI solutions focus on making the mission workforce more informed, effective, and engaged through intelligent automation, agentic workflows, and enhanced decision\-making.
Qualified candidates will combine deep knowledge of large language models and prompt engineering with strong architectural skills for scalable, secure AI systems in regulated government environments. We place particular emphasis on building governed LLM applications for the science, defense, and broader government domains, using OpenAI, Gemini, and Anthropic models delivered through Azure and integrated into Microsoft 365, Copilot, and the Power Platform. We value depth across the areas below and expect candidates to demonstrate experience in a meaningful subset of them.
Key Responsibilities
- Architect and develop large language model applications using OpenAI GPT, Google Gemini, and Anthropic Claude models served through Microsoft Foundry, with a clear understanding of how to select and route between frontier models for a given mission workload.
- Design and build solutions that align with and extend GenAI.mil, the Department of War's enterprise generative AI platform, including agentic workflows and use cases certified for Controlled Unclassified Information and Impact Level 5 environments.
- Design and implement Retrieval\-Augmented Generation systems using Microsoft\-native components, including Azure AI Search for vector and semantic retrieval, document processing pipelines, and grounding strategies that reduce hallucination on authoritative mission data.
- Build and customize Microsoft Copilot integrations, including Copilot Studio agents, declarative and custom copilots, and extensions developed with Semantic Kernel.
- Develop AI orchestration and multi\-step agent workflows on the Microsoft stack, integrating frontier models with Microsoft 365, Dataverse, and the Power Platform to automate complex mission and back\-office processes.
- Implement advanced prompt engineering, function and tool calling, and AI agent architectures for intelligent automation, with attention to evaluation, observability, and governance.
- Design and deploy scalable AI APIs using Python and FastAPI, integrated with Azure OpenAI and Microsoft Foundry model endpoints, and packaged for deployment into GCC High and other accredited cloud environments.
- Collaborate with data and mission subject\-matter experts to tune retrieval, optimize prompts and context strategies, and continuously improve AI system performance against mission outcomes.
- Take part in the Agile process and collaborate effectively within cross\-functional AI delivery teams.
- Build strong internal relationships, mentor others, and evangelize AI best practices, responsible AI guardrails, and emerging capabilities across customer organizations and internally.
- Interpret client requirements and recommend optimal AI solutions, model selection, and implementation strategies for the Microsoft and GenAI.mil ecosystem.
- Support full\-lifecycle AI delivery from requirements gathering through deployment, accreditation support, and maintenance.
Requirements:
Must be a U.S. citizen.
- Must be able to obtain DoD security clearance (Secret or Above).
- Bachelor's degree in Computer Science, Data Science, AI/ML, or equivalent work experience.
- 5\+ years of software development experience, with 2\+ years focused on AI/ML applications and LLM integration.
- Hands\-on experience building applications with frontier large language models, specifically OpenAI GPT and Anthropic Claude, including an understanding of model selection and routing across providers.
- Strong experience building Retrieval\-Augmented Generation systems and semantic search applications.
- Proficiency with vector and semantic retrieval using Azure AI Search, including embedding storage, indexing, and hybrid search.
- Advanced prompt engineering skills with hands\-on experience in function and tool calling and AI agent development.
- Experience building AI orchestration and multi\-step agent workflows, particularly with Semantic Kernel and Microsoft Foundry agent tooling.
- Strong Python programming skills, with experience building AI APIs using FastAPI.
- Experience with Microsoft Copilot Studio, Semantic Kernel, and custom or declarative copilot development.
- Hands\-on experience with cloud AI services, specifically Azure OpenAI and Microsoft Foundry model endpoints.
- Proficiency with a modern frontend framework (React preferred) for AI application user interfaces, including those embedded in Microsoft and Power Platform experiences.
- Experience with containerization (Docker) and orchestration (Kubernetes / Azure Kubernetes Service) for AI service deployment in accredited environments.
- Working knowledge of embedding models, transformer architectures, and fine\-tuning techniques.
- Understanding of AI safety, responsible AI practices, and bias mitigation, with the ability to apply mission\-grade guardrails consistent with Department of Defense standards.
- Experience with version control, CI/CD pipelines (Azure DevOps or GitHub), and MLOps practices for AI system deployment.
- Strong understanding of data preprocessing, document chunking, and knowledge management for RAG systems.
- Familiarity with Agile development methodologies and experience working in collaborative AI delivery teams.
Preferred Qualifications (not required, but a plus)
- Familiarity with developing solutions in the context of GenAI.mil
- Experience delivering on Microsoft Power Platform and Dataverse, and integrating AI capabilities into Microsoft 365 and Power Apps.
- Hands\-on experience in GCC High or other accredited government cloud environments.
- Experience with AI orchestration frameworks such as LangChain or LlamaIndex.
- Experience with open\-source LLMs such as Llama, Mistral, or Falcon, and local or self\-hosted model deployment.
- Knowledge of model fine\-tuning techniques, including LoRA, QLoRA, and PEFT methods.
- Experience with multimodal AI applications combining text, image, and audio.
- Familiarity with AI governance frameworks and compliance requirements for government applications.
- Experience with the Hugging Face ecosystem, model deployment, and custom model training.
- Knowledge of graph databases (such as Neo4j) for knowledge\-graph applications.
- Microsoft Azure AI certifications (for example, Azure AI Engineer Associate) or comparable cloud AI credentials.
- Experience with real\-time AI systems and streaming data processing.
- Background in natural language processing, computer vision, or speech recognition.
Equal Opportunity Employer
HyerTek is an Equal Opportunity Employer. All employment decisions will be made without regard to age, race, creed, color, religion, sex, national origin, ancestry, disability status, veteran status, sexual orientation, gender identity or expression, genetic information, marital status, citizenship status, or any other basis as protected by federal, state, or local law.
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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At HyerTek Inc., 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
HyerTek Inc. AI Hiring
HyerTek Inc. has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Rockville, MD, US.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.