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About This Role
HarmonyTech is currently looking for a talented and self\-motivated Principal AI/ML Engineer specializing in the technology stack given below. You will be working with a high\-performing agile team leading, designing and implementing AI/ML and business intelligence solutions for a Federal Regulatory agency.
Position is available in Northern VA, requires US citizenship and ability to obtain public trust clearance.
Responsiblities:* Lead an agile technical team developing AI/ML and Business Intelligence solutions.
- Business Analysis \& Project Support – requirements elicitation, process mapping, gap analysis, user story development, risk/issue/dependency tracking
- Business Transformation – use\-case documentation, data sensitivity/compliance assessments, stakeholder interviews, change management
- Systems \& Technology Analysis – architecture/dependency mapping, integration analysis, configuration management, workflow diagnostics
- AI/ML Testing, Validation \& Documentation – test plan execution, model inventories, model cards, explainability artifacts, drift/anomaly monitoring, AI governance documentation
- Solution Design \& Development – translating requirements into technical specs, workflow automation builds, data transformation design.
Technology Stack:
*Required** Amazon Sagemaker, Python, Amazon SageMaker Studio
- Microsoft Power Automate, Power BI
- DBeaver
- JIRA, GitHub, ServiceNow
Preferred* SharePoint Online
Education, Experience and Skills:
*Required** Bachelor’s degree in computer science or a related field
- Minimum 5 years of hands\-on experience designing, implementing and maintaining AI/ML models out of which at least 2 years with AWS Sagemaker. Hands\-on experience with Python and Machine learning libraries such as TensorFlow and Keras.
- Experience with data analysis, data cleansing techniques, feature engineering, model design, model validation, with good understanding of model metrics
- Experience with one or more of the following types of models – Linear regression, Logistic regression, Time series analysis and forecasting
- At least 5 years of experience as a hands\-on lead developer, developing code and managing projects using required technology stack (see below).
- Expert knowledge of machine learning and deep learning algorithms and Python libraries
- Expert knowledge of Microsoft Power BI and Power Automate
- At least 2 years of hands\-on experience in Power Automate, Power BI and DBeaver
- Ability to communicate clearly with business users, the team, and leadership
- Innovative, results\-oriented and focused on delivering on time and with quality
Preferred* Experience working with regulatory agencies such as OCC, SEC or FDIC
- Experience with Agile SDLC and tools
- Experience with methodologies such as CRISP\-DM
About HarmonyTech
We have been delivering information technology services and solutions across the federal government and commercial clients for over a decade. Our employees are the most important assets of our company because they deliver value and care for our clients. We are a company of passionate technologists, constantly evolving in our understanding and application of technology to best fulfill our clients' mission needs. We operate under a flat and efficient organizational structure to ensure our hand\-picked consultants have the flexibility to take risks and be innovative. We typically work in small, agile teams as we design and develop solutions to address our clients' business challenges. Our success is measured with every satisfied customer and employee.
Why you want to join us
- You have a passion for solving our customers' complex business problems
- Awesome learning and professional development opportunities
- A culture built on teamwork and excellence
Benefits
HarmonyTech offers a highly competitive salary and benefits package, including:
- Medical/Dental/Vision Insurance
- Short/Long Term Disability Coverage
- Life and AD\&D Insurance
- 401(k) Retirement Plan with Company Match
- Tuition Reimbursement
- Employee Referral Bonus
- Paid Federal Holidays
- Accrued Paid Time Off
- View our full benefits package
If you are interested and feel that you would excel in the position, we invite you to apply. During this phase of our recruiting effort, we will not be able to accept telephone calls. Only those candidates meeting the requirements will be contacted. No recruiters, please.
Legal
HarmonyTech believes in a policy of equal employment and opportunity for all people based on merit. We are an Equal Opportunity Employer (EEO) and Drug Free Workplace Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, protected veteran status, disability status, or any other category protected by applicable federal, state, or local laws.
The statements herein are intended to describe the general nature and level of work being performed by employees and are not to be construed as an exhaustive list of responsibilities, duties, and skills required of personnel. Additionally, they do not establish a contract for employment and are subject to change at the discretion of HarmonyTech.
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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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At HarmonyTech, 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 $214,900 based on 6,420 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 $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
HarmonyTech AI Hiring
HarmonyTech has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Fairfax, VA, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 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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