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About This Role
Everforth ECS is seeking a AI/ML Engineer to work in an onsite capacity with primary duty located at the Pentagon in Arlington, VA. Note: This role is contingent upon additional funding.
Everforth ECS Federal has an immediate opening for a Technical Lead supporting the PSP2 Agentic Network program for a Department of Defense customer at the Pentagon in Arlington, Virginia
- The Technical Lead serves as the senior hands-on technical authority for the PSP2 Agentic Network. The role designs, prototypes, deploys, and sustains secure agentic AI, data, and platform capabilities across classified and unclassified environments, with a primary focus on JWICS-hosted mission services and approved information-sharing paths.
- The Technical Lead evaluates and improves hosting architectures, including the existing JWICS-hosting option and cloud landing-zone alternatives, to improve reliability, support responsiveness, model inference availability, access management, auditability, and lifecycle control. The role is responsible for turning approved technical approaches into deployable, observable, and supportable systems.
- The position engineers an unclassified platform for storing and sharing releasable information with allies and partners and develops technically feasible patterns for moving approved data into JWICS through authorized cross-domain solutions. The Technical Lead defines the security, interface, data-governance, and operational constraints required for each implementation.
- The Technical Lead is based at the Pentagon and supports technical integration, testing, deployment, troubleshooting, and sustainment activities at Combatant Commands and other Government-directed locations. Routine domestic and international travel is required.
Key Responsibilities
- Manage, supervise, and lead the technical architecture for the PSP2 Agentic Network, including agent orchestration, model inference, data access, APIs, platform services, network boundaries, observability, and secure deployment patterns.
- Assess the technical suitability, performance, resilience, support model, and operational constraints of available JWICS hosting options and document recommended architecture patterns and implementation tradeoffs.
- Deploy and operate LLM inference and agentic AI services, including model-serving endpoints, prompt and policy controls, retrieval and tool-use integrations, scaling, performance tuning, monitoring, and failure recovery.
- Implement a dedicated unclassified platform for controlled storage, discovery, access, and sharing of releasable data with approved allies and partners.
- Analyze data flows between unclassified and JWICS environments; define data preparation, labeling, format, security, release, and interface requirements for movement through authorized cross-domain solutions.
- Develop technical designs, integration specifications, and proof-of-concept implementations for approved cross-domain data-transfer patterns without bypassing established security, release, or authorization processes.
- Implement application and platform observability, including health checks, logging, metrics, tracing, alerting, performance analysis, incident diagnostics, and operational dashboards.
- Perform technical hardening, integration testing, performance testing, security validation, and operational-readiness verification before release to users.
- Troubleshoot complex issues involving environment configuration, software dependencies, network connectivity, identity and access controls, interfaces, containerization, data pipelines, model inference, and deployment processes.
- Produce and maintain architecture diagrams, interface-control documentation, configuration baselines, runbooks, troubleshooting procedures, deployment instructions, and technical decision records.
- Evaluate architectural alternatives and clearly articulate technical tradeoffs involving security, reliability, performance, scalability, interoperability, maintainability, and cost.
- Provide technical evidence, configuration information, and architecture artifacts required to support cybersecurity, accreditation, and Authorization to Operate activities.
- Review technical implementations, establish engineering standards, and provide technical direction to developers and engineers working on the PSP2 Agentic Network.
- Provide on-site and remote technical support during integration, deployment, initial operational use, and stabilization periods.
Salary is commensurate with skillset, qualifications, experience, and educational background.
Salary Range: $125,000-145,000
General Description of Benefits
Requirements:
- U.S. Citizen.
- Current DoD Top Secret clearance with eligibility for access to Sensitive Compartmented Information (TS/SCI), with the ability to obtain and maintain required facility, system, enclave, and JWICS access.
- Active AWS Cloud and Security+ Certifications.
- Bachelor's degree in Computer Science, Information Technology, Software Engineering, Systems Engineering, Cybersecurity, or a related technical field is required. A master's degree is preferred.
- 5+ years of relevant professional experience supporting technical integration, deployment, test, fielding, or sustainment activities.
- Ability to work onsite at the Pentagon in Arlington, VA.
- Ability to attend meetings and program events in Fairfax, VA*.*
- Ability to travel <50% throughout the lifespan of the Program to CONUS/OCONUS customer sites and government installations.
- Demonstrated professional experience integrating, deploying, troubleshooting, or sustaining software, data, analytics, AI-enabled, or mission-platform capabilities within DoD, Intelligence Community, or other classified Government environments.
- Familiarity with the Maven Smart System and comparable CDAO, DoD, or Intelligence Community mission platforms.
- Experience integrating third-party software, data sources, models, agents, interfaces, or operational tools into an existing Government platform.
- Experience supporting technical integration and deployment across multiple security enclaves, including NIPR, SIPR, JWICS, or comparable classified environments.
- Experience troubleshooting complex technical issues involving environment configuration, software dependencies, network connectivity, access controls, interfaces, containerized applications, platform services, and deployment processes.
- Experience coordinating technical-readiness activities involving network or enclave access, credentials, user and service accounts, Government-furnished equipment and information, platform dependencies, and security or accreditation actions.
- Ability to identify and isolate the source of technical problems, distinguish Contractor-controlled issues from Government dependencies, recommend corrective actions, and document escalation rationale.
- Ability to operate within time-sensitive issue-resolution requirements and manage multiple technical blockers, dependencies, and deployment priorities simultaneously.
- Experience supporting software or platform hardening, integration testing, user acceptance testing, operational-readiness assessments, demonstrations, pilots, or fielding events.
- Understanding of technical and operational considerations associated with cross-domain deployment, classified data handling, system interfaces, access controls, and approved information-transfer paths.
- Ability to assess architectural, cybersecurity, sustainment, reliability, performance, and scalability implications of proposed technical approaches.
- Ability to analyze and document technically feasible data flows from unclassified environments to JWICS while preserving required security, release, and authorization controls.
- Experience documenting technical risks, dependencies, environment configurations, test results, defects, corrective actions, deployment status, and sustainment requirements.
- Ability to translate complex technical findings into clear operational impacts, decision points, and recommended actions for engineering teams, Government leads, and non-technical stakeholders.
- Proven ability to coordinate effectively across Government organizations, operational commands, engineering teams, vendors, network and security personnel, platform owners, and data owners.
- Strong written, verbal, briefing, and technical-documentation skills, including the ability to prepare issue summaries, readiness assessments, decision-support materials, technical procedures, and senior-leader briefings.
- Strong analytical, organizational, troubleshooting, risk-management, and stakeholder-coordination skills.
- Ability to work independently in a classified, multi-vendor, Government-led development environment with evolving priorities, compressed timelines, and mission-critical requirements.
- Proficiency with Microsoft Office applications, collaboration platforms, issue-tracking tools, technical documentation repositories, and standard Government staff-coordination tools.
- Ability and willingness to perform routine domestic and international travel in support of site assessments, integration, testing, demonstrations, deployments, and initial operational use.
- Strong problem-solving and decision-making capabilities, with a proven ability to weigh the relative costs and benefits of potential actions and identify the most appropriate solution.
- Highly developed interpersonal and oral/written communication skills, with the ability to effectively and professionally interact with a diverse set of stakeholders (from peers to end-users to executive management).
Req Benefits: Benefits - Everforth ECS
Salary Context
This $125K-$145K range is in the lower quartile 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 Everforth ECS, 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 ($135K) sits 36% below the category median. Disclosed range: $125K to $145K.
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.
Everforth ECS AI Hiring
Everforth ECS has 5 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer, Prompt Engineer. Positions span Macdill AFB, FL, US, Washington, DC, US, Remote, US. Compensation range: $140K - $200K.
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.
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