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About This Role
We are seeking an AI Solutions Engieer to join our team.
TekSynap is a fast\-growing high\-tech company that understands both the pace of technology today and the need to have a comprehensive well planned information management environment. “Technology moving at the speed of thought” embodies these principles – the need to nimbly utilize the best that information technology offers to meet the business needs of our Federal Government customers.
We offer our full\-time employees a competitive benefits package to include health, dental, vision, 401K, life insurance, short\-term and long\-term disability plans, vacation time and holidays.
Visit us at www.TekSynap.com.
Apply now to explore jobs with us!
The safety and health of our employees is of the utmost importance. Employees are required to comply with any vaccination requirements mandated by contract, applicable law or regulation.
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RESPONSIBILITIES* Architect solutions to accommodate complex business needs.
- Design enterprise architecture that aligns technologies across network, compute, storage, platform, cloud, database, software, systems, security, mail, and other technology domains.
- Work with teams to improve business and technology processes through technology using standard industry best practices.
- Support numerous large projects simultaneously.
- Assist with future planning.
- Stay current on standard architecture frameworks for the Federal Government.
- Assist with building AI capabilities used by end\-users, administrators, and the application development community.
- Design and configure enterprise AI tools, including agentic harnesses and chatbots.
- Define and refine strategies for efficient token usage.
- Support AI\-enabled development of mission applications by troubleshooting integration issues, API errors, and system reliability concerns.
- Train the user community on best practices, limitations, and advanced AI capabilities.
- Recommend new AI capabilities and options to executives as industry capabilities evolve.
- Monitor the service health and cost associated with AI usage.
REQUIRED QUALIFICATIONS* Bachelor's degree required. A Bachelor's degree may be substituted with six (6\) additional years of related experience.
- Fifteen (15\) years of IT experience.
- At least five (5\) years of experience in Enterprise Architecture and its components.
- Minimum of ten (10\) years of application development experience.
- Strong background in traditional application development practices.
- Expertise configuring an agentic harness.
- Understanding of the business challenges associated with adopting AI.
- Experience with frontier models and tools such as Claude Code, Codex, OpenCode, or Cursor.
- Extensive knowledge of database, middleware, and front\-end technologies.
- Proven track record of application engineering, providing strategic direction, and fostering a collaborative and innovative work environment.
WORK ENVIRONMENT AND PHYSICAL DEMANDS
The work environment characteristics described here are representative of those an employee encounters while performing the essential functions of the job. Reasonable accommodation may be made to enable individuals with disabilities to perform the essential functions.
- Location: Washington, DC
- Type of environment: Office
- Noise level: Medium
- Work schedule: Schedule is day shift Monday – Friday. May be requested to work evenings and weekends to meet program and contract needs.
- Amount of Travel: Less than 10%
PHYSICAL DEMANDS
The physical demands described here are representative of those that must be met by an employee to successfully perform the essential functions of this job. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.
While performing the duties of this job, the employee is regularly required to use hands to grip, handle, or feel; reach with hands and arms; and talk or hear. The employee is regularly required to stand; walk; sit; climb or balance; and stoop, kneel, crouch, or crawl. The employee is regularly required to lift up to 10 pounds.
The employee is frequently required to lift up to 25 pounds; and up to 50 pounds. The vision requirements include close vision, distance vision, peripheral vision, depth perception, and ability to adjust focus. WORK AUTHORIZATION/SECURITY CLEARANCE* Candidate must be a U.S. Citizen
- Must be able to obtain a Public Trust
WAGE INFORMATION
Target salary range: $160,000\.00 \- $190,000\.00\. The salary range displayed is an estimate only and is not a guarantee of compensation or salary, and will be determined on several factors regarding the individual’s particular combination of education, knowledge, skills, competencies and experience, as well as contract parameters and organizational requirements. The displayed salary is one component of the total compensation package for employees. OTHER INFORMATION
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.
TekSynap is a drug\-free workplace. We reserve the right to conduct drug testing in accordance with federal, state, and local laws. All employees and candidates may be subject to drug screening if deemed necessary to ensure a safe and compliant working environment.
Many positions require specific certifications and/or the ability to obtain a security clearance. Security clearances may only be granted to U.S. citizens. Applicants who accept an offer of employment may be subject to investigations performed by TekSynap and/or the Government to verify the candidate meets the required qualifications and other eligibility requirements. EQUAL EMPLOYMENT OPPORTUNITY
In order to provide equal employment and advancement opportunities to all individuals, employment decisions will be based on merit, qualifications, and abilities. TekSynap does not discriminate against any person because of race, color, creed, religion, sex, sexual orientation, gender identity, protected veteran status, national origin, disability, age, genetic information or any other characteristic protected by law (referred to as “protected status”). This nondiscrimination policy extends to all terms, conditions, and privileges of employment as well as the use of all company facilities, participation in all company\-sponsored activities, and all employment actions such as promotions, compensation, benefits, and termination of employment.
TekSynap is committed to ensuring that our online application process provides an equal employment opportunity to all job seekers, including individuals with disabilities. If you believe you need a reasonable accommodation in order to search for a job opening or to submit an application, please contact [email protected] for assistance.
Salary Context
This $160K-$190K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At TekSynap, 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($175K) sits 20% below the category median. Disclosed range: $160K to $190K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
TekSynap AI Hiring
TekSynap has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Washington, DC, US. Compensation range: $190K - $190K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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