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Who We Are:
Exiger is the AI partner for supply chain and procurement automation. Its centralized 1EXIGER.AI platform allows organizations to manage their entire operating network, from parts to suppliers, regulators, and customers, through an autonomous agentic workforce that learns, adapts, and augments with every decision. The AI-native platform, combined with the largest supply chain knowledge asset, empowers 550+ global customers, including 150 Fortune 500 and 80+ government and defense industrial organizations, to act first. Exiger is FedRAMP® authorized and a 2x Leader in Gartner® Magic Quadrant™ for Supplier Risk Management.
Senior Manager, Marketing Operations & AI Enablement
Location: McLean, VA or Richmond, VA
Work Environment: Hybrid
The Opportunity
The Senior Manager, Marketing Operations & AI Enablement will independently lead day-to-day marketing operations across campaign execution, email marketing, automation, lead management, reporting, and AI-enabled workflow improvement. This role owns the ongoing operation, optimization, troubleshooting, and continuous improvement of the marketing systems, processes, and programs that help marketing run efficiently, improve performance, and scale execution.
The ideal candidate is a curious, technically skilled operator who can identify opportunities to improve how work gets done, optimize existing processes, and apply AI and automation in practical, measurable ways. This role will play a key part in improving marketing efficiency, strengthening email and campaign performance, and helping the team adopt smarter, more scalable ways of working.
What You Will Do
Marketing Operations Execution
- Manage day-to-day marketing operations across campaign workflows, lead routing, SLA alignment, data quality, and reporting.
- Support the execution and continuous improvement of processes that keep marketing programs running smoothly and efficiently.
- Help maintain consistent workflows, standards, and documentation across campaign and operational activities.
Email Marketing and Lifecycle Optimization
- Own and improve email marketing operations, including audience segmentation, testing, personalization, send optimization, performance analysis, and workflow automation.
- Support the development and optimization of nurture and lifecycle programs that improve targeting, engagement, and conversion.
- Apply testing and data analysis to improve email strategy and execution over time.
- Ensure email programs follow applicable consent, and privacy regulations and are in line with brand standards.
AI Enablement and Process Improvement
- Identify opportunities to improve existing marketing processes through AI, automation, and smarter workflow design.
- Design, build, and refine AI-assisted workflows for campaign intake, email production, segmentation, QA, lead management, reporting, and post-campaign analysis.
- Support the rollout of AI-enabled ways of working by turning team priorities into practical, repeatable processes.
- Test new tools and approaches where they can improve speed, quality, consistency, or performance.
Automation, Lead Management, and Reporting
- Manage and optimize marketing automation processes, including lead scoring/routing, form strategy, nurture flows, attribution support, and the AI SDR program.
- Maintain dashboards and reporting that track funnel performance, email effectiveness, campaign performance, process efficiency, and workflow improvements.
- Help ensure marketing data is accurate, actionable, and aligned to reporting needs across teams.
- Identify bottlenecks in campaign, email, and content operations and recommend improvements to reduce manual effort and increase throughput.
Cross-Functional Support and Enablement
- Partner with RevOps, Sales, IT, and Legal to support compliant and effective use of marketing automation and AI.
- Document workflows, standards, and best practices to improve consistency and scalability across the team.
- Support team adoption of new tools, processes, and AI-enabled ways of working through documentation, training, and hands-on problem solving.
What You Need
- Bachelor's degree in Marketing, Business, Communications, Information Systems, or a related field.
- 5+ years of experience in marketing or revenue operations
- Strong experience with MAP, CRM, ABM, marketing automation, analytics, and lead management.
- Hands-on experience with email marketing strategy and operations, including segmentation, testing, deliverability, and performance optimization
- Experience implementing AI or automation within marketing workflows
- Experience designing, building, and optimizing workflows from start to finish.
- Strong technical troubleshooting skills, with the ability to independently identify root causes and resolve workflow, system, and automation issues.
- Ability to work independently and take ownership of operational execution and improvement.
- Strong analytical and technical skills. Experience working with APIs is preferred, knowledge of HTML is required.
- Demonstrated curiosity and willingness to test, learn, and improve processes using new tools and technologies
- Strong communication and cross-functional collaboration skills.
Why Work at Exiger
- A high-performance culture grounded in accountability, collaboration, and shared standards.
- Discretionary time off.
- Health, vision, and dental benefits.
- Competitive compensation package.
- 16 weeks of fully paid parental leave.
- A flexible approach to home and office work, where applicable.
- Wellness stipends and dedicated wellness programming.
- Career development support and reimbursement for approved certifications.
#Li-Hybrid
Exiger is named a Leader in the Gartner® Magic Quadrant™ for Supplier Risk Management, twice selected as one of Fast Company's 'Brands That Matter,' and recipient of the Third Party Risk Association's Innovator Award, Exiger's technology has been recognized by leading analyst evaluations and 50+ awards. Learn more at Exiger.com and follow Exiger on LinkedIn.
At Exiger, our values define how we work and why we lead. We are mission-inspired, imagination-driven, trust-anchored, and compassion-focused—committed to building technology that makes the world safer, more transparent, and more resilient.
All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability or protected veteran status, or any other legally protected basis, in accordance with applicable law.
Exiger's hybrid work policy is periodically reviewed and adjusted to align with evolving business needs.
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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Exiger, 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 in Demand for This Role
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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Exiger AI Hiring
Exiger has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Richmond, VA, US, McLean, VA.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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