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About This Role
About Reingold
Reingold is a full-service marketing and creative firm in Alexandria, VA. For more than 40 years, we’ve been driven by a simple mission: to help organizations –– whether in the government, nonprofit, or business sector –– as they strive to make the world a better place. We hire top-notch talent to develop and launch compelling, inventive communications campaigns, leveraging the latest technology and digital media strategies that enable our clients to meet their audiences where they are.
We’re proud to say we’ve built a team of curious, passionate pros — from marketers and tech wizards to designers and strategists — who love what they do and bring their A-game every day. We take our work seriously, but not ourselves. We believe great ideas come from collaboration, humor, and a little bit of fun along the way.
When you work at Reingold, you get more than a job, you get a community. We offer competitive salaries, a comprehensive benefits package, a dynamic hybrid work environment, a vibrant workplace and growth opportunities in a variety of specialty areas. That means when we find great people (and they find us), they stick around for the long term.
That’s where you come in. Reingold is seeking a Lead Agentic Software Engineer to build the platforms our clients run on — content management systems, e-commerce, web applications, and integrations — and to bring AI agents and automation into that work for our government, nonprofit, and commercial clients. This is a full-time, exempt position in Reingold’s Digital department, reporting to the Senior Director, Digital.
You’ll spend most of your time as a hands-on, billable engineer on client projects: architecting and shipping production applications and platform builds, and applying agentic AI — LLM-powered agents that plan, call tools, and carry out multi-step work — to client products, delivery pipelines, and internal operations. The rest is reserved for capability building: evaluating emerging AI tools and models, developing reusable components and internal tooling, and setting engineering and quality standards that scale across teams. It’s a senior individual-contributor role that blends deep platform engineering with practical, production-grade AI.
This is a hybrid role, requiring at least two days per week on site at Reingold’s headquarters in Alexandria, VA.
Here’s the work you’ll do:
Essential Functions:* Bring AI agents and automation into client products, delivery pipelines, and internal operations — architecting agentic systems end to end: model and framework selection (e.g., Anthropic, OpenAI), tool and function calling, multi-step orchestration, context and memory, retrieval-augmented generation with embeddings and vector stores, and guardrails and fallback behavior — choosing approaches based on accuracy, latency, and cost.
- Design, build, and ship production web applications, platforms, and integrations for client programs — architecting the full stack across modern JavaScript frameworks (React, TypeScript), server-side services, and relational and NoSQL data stores.
- Define and run evaluations of agent behavior — task success, tool-call correctness, regression, reliability, and cost — and iterate on production systems based on measured results, holding the work to a real production bar rather than demos.
- Establish responsible-AI and data-governance practices for public-sector and regulated work: secure handling of client data, human-in-the-loop review, and safeguards against hallucination, prompt injection, data leakage, and inappropriate disclosure.
- Lead architecture and development on enterprise content management platforms — traditional and headless alike, including WordPress, Umbraco/.NET, Adobe Experience Manager, and comparable systems — and integrate them with AI services, third-party APIs, and client data.
- Design and build e-commerce solutions on platforms such as Adobe Commerce, Shopify, or WooCommerce: storefront, catalog and order management, checkout and payment integration, and connections to fulfillment, CRM, and analytics.
- Deploy and operate applications on a major cloud platform: CI/CD pipelines, containerized or serverless services, monitoring, and cost management.
- Own quality on your work: build automated end-to-end, regression, and API test suites (for example with Playwright or Cypress) with CI quality gates, and implement automated accessibility testing against Section 508 and WCAG 2.1 AA (for example with axe or WAVE), leading remediation — applying AI-assisted techniques such as test generation, visual regression, and defect triage where they help.
- Evaluate emerging AI platforms, models, and tools — including agentic coding tools such as Claude Code, Cursor, and GitHub Copilot — recommend adoption based on testing in real Reingold workflows, and build reusable components and internal tooling that teams can apply across engagements.
- Partner with strategists, designers, account teams, and clients to turn business goals into technical requirements and explain tradeoffs to non-technical audiences — providing technical direction and code review on projects, acting as technical lead when we’re building agentic tooling for a client, and mentoring developers and sharing what you learn across the agency.
This job description is not designed to cover or contain a comprehensive listing of activities, duties or responsibilities that are required of the employee. Duties, responsibilities and activities may change, or new ones may be assigned at any time.
This role is a good fit for someone with these qualifications, experience and skills:
Required Qualifications:* Bachelor’s degree in computer science, software engineering, information systems, or a related field, or equivalent professional experience, with seven or more years building and shipping production web applications and platforms.
- Deep experience delivering and maintaining sites or applications on one or more enterprise content management platforms — for example WordPress, Umbraco/.NET, Adobe Experience Manager, or a headless CMS — with a track record of picking up unfamiliar platforms quickly.
- Strong full-stack experience with modern JavaScript frameworks (React, TypeScript), API design, and both relational and NoSQL databases.
- Hands-on experience engineering e-commerce solutions, including storefront, checkout and payment integration, catalog or order management, and reporting.
- Production experience building with large language models — API integration, prompt engineering, retrieval-augmented generation, and agent or workflow automation — deployed to real users, not just prototypes.
- Experience designing and implementing automated testing, including end-to-end or regression suites in CI pipelines.
- Clear technical communication, in writing and in person, with both technical and non-technical audiences.
- U.S. citizenship, with the ability to obtain and maintain a U.S. government suitability or clearance determination if required for a project assignment.
Preferred Qualifications:* Experience with cloud AI services such as Google Vertex AI, Azure AI, or Amazon Bedrock.
- Experience delivering software in federal, state, or nonprofit environments, including work subject to security review, authority to operate (ATO) processes, or Section 508 compliance.
- Familiarity with the Model Context Protocol (MCP), vector databases, and agent evaluation or observability tooling.
- Experience with headless and decoupled architectures, including headless CMS platforms and front-end frameworks such as Next.js.
- Experience with DevOps practices and infrastructure as code, such as Terraform, Docker, and GitHub Actions.
- Depth in one or more enterprise commerce or experience platforms at scale — such as Adobe Commerce (Magento), Shopify Plus, Salesforce Commerce Cloud, or Adobe Experience Manager — and familiarity with PCI DSS and secure handling of payment data.
- Experience contributing to proposals, technical scoping, or estimates, and presenting technical work to clients.
Supervisory Responsibilities: This position may supervise one or more employees.
Work environment and physical requirements: Prolonged periods sitting at a desk and working on a computer.
Travel: Travel is minimal. Occasional local travel to client sites in the Washington, DC metropolitan area may be required.
Additional Information:
The base salary range of $120,700.00 to $193,100.00 represents the low and high of the company’s pay range for this position. The budgeted starting salary is in the lower half of the base salary range. Individual compensation will vary based on experience, skills, qualifications, internal equity, and business needs. Full-time employees are eligible for comprehensive health and wellness benefits, 401(k) employer contributions and matching, and other benefits.
Reingold is dedicated to providing equal opportunity for individuals of all abilities. If you require assistance or need accommodation at any stage of the application process because of a disability or medical condition, please feel free to email [email protected] or contact a member of the People & Experience team at 202-333-0400.
Reingold has a policy of maintaining a workplace free of drugs and alcohol. For access to the full policy, which is part of the Reingold Employee Handbook, email [email protected]. Reingold ensures a safe, healthy, and productive work environment for its employees and others. All applicants are advised that full compliance with this policy is a condition of employment at Reingold.
Applicants must have authorization to work for any employer in the United States. We are currently unable to sponsor or to take over sponsorship of an employment visa. U.S. citizenship may be required for project assignments that involve access to sensitive or classified information. Candidates must be able to obtain and maintain a U.S. government security clearance if required.
Reingold is proud to be an Equal Opportunity Employer. We invite applications from all interested individuals including veterans and persons with disabilities.
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Salary Context
This $120K-$193K range is below the median for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Reingold 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 $212,000 based on 11,857 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($156K) sits 26% below the category median. Disclosed range: $120K to $193K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Reingold Inc AI Hiring
Reingold Inc has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Alexandria, VA, US. Compensation range: $193K - $193K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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