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About This Role
SMX is standing up and scaling an agentic AI practice: bringing agentic software engineering (autonomous AI agents that write, test, and modernize code) to Department of War, Federal Civilian, and Intelligence Community customers, with SMX's accreditation and authorization platform as the control and management plane. This role is the deputy to the practice owner and the field\-facing technical person for the practice. You are more technical than a seller, more commercial than an architect, and you can build. You carry the load across the full scope of the practice: setting technical direction, shaping and closing opportunities, prototyping and validating solutions with your own hands, and representing the practice owner in customer, partner, and internal forums. You will partner closely with SMX's growth, delivery and technical organizations, which owns their respective parts of execution, while you own the technical field and the go\-to\-market motion. This is a remote position supporting a Herndon, VA based team.
Essential Duties and Responsibilities
- Second in Command for the practice owner. Represent the practice and carry decisions in customer, partner, and internal settings when the owner is not in the room, and help set and drive the practice's technical and commercial direction.
- Own the technical narrative across the full opportunity lifecycle, from qualification to close, as the practice's lead technical seller.
- Demo, personally. Deliver compelling demonstrations of agentic software engineering platforms (such as autonomous coding agents) to technical and program stakeholders in customer settings.
- Prototype, integrate, and validate agentic AI solutions hands\-on to prove feasibility and de\-risk delivery before commitments are made to a customer.
- Understand the reference architecture. Develop and communicate architectures that integrate agentic AI with SMX's authorization platform and customer mission systems, articulating the path to ATO across FedRAMP High, IL5, IL6, and air\-gapped environments.
- Shape practice strategy. Contribute to offering definition, partner strategy, pipeline prioritization, and the go\-to\-market motion.
- Set technical direction; hand off the build. Direct and validate feasibility in partnership with SMX solutions architects and the delivery organization, who own execution.
- Drive cross\-functional execution across capture, growth, delivery, and partner functions to shape opportunities well and execute strongly against commitments.
- Author and review proposals as a technical lead, and identify the technical resource needs required for compliant, winning responses.
- Build the pipeline. Advance a qualified AI and agentic solutions pipeline of DoW opportunities, with Federal Civilian and IC pursuits as growth vectors.
- Run partner co\-sell responsibly. Support co\-sell with technology and cloud partners (for example, AWS Marketplace / ACE), navigating partner\-neutrality and organizational\-conflict\-of\-interest considerations, and resourcing SME and delivery commitments before reaffirming them to customers or partners.
- Represent SMX externally in customer briefings, industry panels, and thought\-leadership sessions, and stay current on advances in agentic AI, LLM tooling, and modern software delivery.
Required Skills \& Experience
- Clearance: Active U.S. Government security clearance, Secret minimum, with Top Secret and SCI eligibility strongly preferred given IC pursuits.
- Education: Bachelor's Degree in Computer Science, a related discipline, or commensurate work experience.
- Experience: At least eight (8\) years in technical sales, sales engineering, or solutions architecture in a corporate or government solutions environment, including time operating at a strategic, executive\-facing level.
- Hands\-on agentic AI tooling: Demonstrated experience with agentic AI / LLM developer tooling (autonomous coding agents such as Devin, or comparable), with the ability to personally build and demo, not only direct others.
- Builder credibility: Able to prototype, integrate, and validate solutions directly, not only whiteboard them.
- Proven technical sales success in high\-security and compliance\-driven environments.
- Experience as a Solution Architect with multiple full life\-cycle customer implementations.
- Demonstrated ability to personally lead customer demonstrations and present reference architectures to technical buyers.
- Experience with modern IT practices, DevSecOps, and cloud operations (AWS, Azure, or GCP).
- Experience with architectural and implementation\-level design and with software development methodologies (Agile, Waterfall, SAFe).
- Excellent verbal, written, and visual communication skills, including the ability to write proposals and present to executives, large groups, and small technical sessions.
- Deep technical curiosity and the ability to problem\-solve and create solutions under ambiguity.
Desired Skills \& Experience
- Experience selling or delivering within DoW / DoD, with Federal Civilian and IC relationships a strong plus.
- Familiarity with federal authorization pathways (FedRAMP, IL4/IL5/IL6, ATO) and AWS GovCloud.
- Prior experience standing up a new practice, partner motion, or solution offering from early stage.
- Comprehensive understanding of hybrid architectures and experience developing solutions across varying infrastructure, coding languages, and applications.
- Experience with Systems Modeling Language (SysML) and architecture frameworks such as the Unified Architecture Framework (UAF) and the Department of Defense Architecture Framework (DoDAF).
- Research and development (R\&D) experience.
\# LISA1
\#CJpost
At SMX®, we are a team of technical and domain experts dedicated to enabling your mission. From priority national security initiatives for the DoD to highly assured and compliant solutions for healthcare, we understand that digital transformation is key to your future success.
We share your vision for the future and strive to accelerate your impact on the world. We bring both cutting edge technology and an expansive view of what's possible to every engagement. Our delivery model and unique approaches harness our deep technical and domain knowledge, providing forward\-looking insights and practical solutions to power secure mission acceleration.
SMX is an Equal Opportunity employer including disabilities and veterans.
Selected applicant may be subject to a background investigation and/or education verification.
SMX does not sponsor a new applicant for employment authorization or immigration related support for this position (i.e. H1B, F\-1 OPT, F\-1 STEM OPT, F\-1 CPT, J\-1, TN, E\-2, E\-3, L\-1 and O\-1, or any EADs or other forms of work authorization that require immigration support from an employer).
Salary Context
This $172K-$272K range is above the 75th percentile 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 SMX, 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. Senior-level AI roles across all categories have a median of $230,000. Disclosed range: $172K to $272K.
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.
SMX AI Hiring
SMX has 3 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Washington, DC, US, ‘Aiea, HI, US. Compensation range: $173K - $272K.
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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