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About This Role
About InvoiceCloud:
InvoiceCloud is a fast\-growing fintech leader recognized with 20 major awards in 2025, including USA TODAY and Boston Globe Top Workplaces, multiple SaaS Awards wins for Best Solution for Finance and FinTech, and national customer service honors from Stevie and the Business Intelligence Group. Judges also highlighted our mission to reduce digital exclusion and restore simplicity and dignity to how people pay for essential services, as well as our leadership in AI maturity and responsible innovation. It's an award\-winning, purpose\-driven environment where top talent thrives. To learn more, visit InvoiceCloud.com.
Job Details:
We are seeking a highly skilled and results\-oriented AI Security Engineer to support the Cybersecurity, Engineering, and Data Science organizations. This role plays a critical part in advancing InvoiceCloud's AI\-first strategy by ensuring that AI/ML and generative AI systems are secure, resilient, compliant, and aligned with business objectives.
This is role operates as a subject matter expert in AI security. The ideal candidate brings deep expertise in application security, AI/ML risk, and cloud\-native security engineering, and serves as a trusted partner to Engineering, Product, DevSecOps, Legal/Privacy, and Security Operations. Success requires strong ownership, structured problem solving, cross\-functional collaboration, and the ability to balance risk reduction with business velocity.
Success Profile:
This role is anchored in our company's core competencies—These competencies reflect the mindsets and behaviors that define success in this role. We outline how each competency translates into real\-world actions and outcomes specific to this role.
Results Driven
- Leads AI Security Architecture \& Secure Design initiatives by designing and implementing lifecycle security controls across data ingestion, training, evaluation, deployment, and monitoring environments to measurably reduce AI\-specific risk while maintaining product velocity.
- Conducts structured Threat Modeling \& Risk Assessment exercises for generative AI, RAG, and agent\-based systems, evaluating risks such as prompt injection, data poisoning, model extraction, model inversion, abuse/misuse, and data leakage, and mapping findings to OWASP Top 10 for LLM Applications, MITRE ATLAS, and NIST AI RMF to drive remediation through engineering teams.
- Defines and operationalizes Monitoring, Detection \& Incident Response capabilities for AI systems by implementing prompt and output telemetry, tool\-call logging, anomaly detection, and AI\-specific incident response playbooks integrated into SIEM/SOC workflows.
- Delivers measurable outcomes aligned to 30\-, 150\-, and 210\-day milestones, including secure reference architectures, hardened AI environments, integrated security controls, and executive\-ready reporting on AI risk reduction and posture maturity.
Takes Ownership
- Establishes and formalizes AI Governance, Privacy \& Third\-Party Risk requirements by defining security expectations for AI use cases, third\-party models, vendor integrations, and sensitive data usage, embedding controls into SDLC, procurement, and engineering standards.
- Drives Cross\-Functional Collaboration \& Enablement by partnering with Engineering, Data Science, DevSecOps, Product, Legal/Privacy, and SOC teams to align on risk appetite, escalation paths, and secure design guardrails while raising AI security maturity across the organization.
- Inventories current and planned AI/ML initiatives, documents system architectures and sensitive\-data touchpoints, and implements a structured AI security intake and risk\-rating process that ensures accountability and transparency.
- Develops and communicates forward\-looking 6\- and 12\-month AI security maturation plans that align technical priorities with business goals and clearly articulate risk trends, metrics, and investment needs to Security leadership and the CISO.
Drives Efficiency
- Integrates Secure MLOps / MLSecOps controls into AI delivery pipelines, including secure model registries, artifact signing and provenance validation, dependency scanning, secrets management, CI/CD guardrails, and hardened training and inference environments across AWS and Azure.
- Builds and scales AI Security Testing \& Red Teaming workflows by creating repeatable adversarial evaluation plans for jailbreaks, model evasion, prompt injection, and data exfiltration scenarios, ensuring security controls remain effective over time.
- Develops automated regression test harnesses to continuously validate AI security protections as models, prompts, and dependencies evolve, reducing manual effort and improving coverage.
- Establishes a sustainable AI security operating rhythm that includes intake reviews, threat modeling checkpoints, remediation tracking, and structured monitoring ownership to bring consistency and order to AI risk management
Innovative
- Advances AI Security Testing \& Red Teaming capabilities through adversarial experimentation and multi\-dimensional analysis, proactively identifying emerging AI threat patterns before production impact.
- Leverages AI and automation to strengthen testing coverage, automate regression validation, enhance anomaly detection logic, and improve the scalability of AI security monitoring and response.
- Continuously evaluates emerging AI security research, tooling advancements, and regulatory developments, translating insights into adaptive defensive controls that support InvoiceCloud's AI\-first strategy while enabling responsible innovation.
Requirements
- Bachelor's degree in Computer Science, Cybersecurity, Engineering, Data Science, or related field (or equivalent practical experience).
- 5\+ years of experience in security engineering, application/product security, cloud security, or DevSecOps.
- 2\+ years of experience building or securing AI/ML systems (including LLM\-based applications) in production environments.
- Strong understanding of AI/ML threats and defenses, including prompt injection, data poisoning, model extraction, model inversion, adversarial inputs, data leakage, and abuse/misuse scenarios.
- Experience integrating security into CI/CD and MLOps pipelines.
- Proficiency with cloud platforms (AWS and Azure), container security, IAM, network segmentation, key management, and secrets management.
- Familiarity with industry guidance such as OWASP GenAI/Top 10 for LLM Applications, MITRE ATLAS, and/or NIST AI RMF preferred.
- Relevant certifications such as CISSP, CSSLP, CCSP, Azure Security certifications, or GIAC certifications preferred.
InvoiceCloud is committed to providing equal employment opportunities to all employees and applicants. We do not tolerate discrimination or harassment of any kind based on race, color, religion, age, sex, nationality, disability, genetic information, veteran or military status, sexual orientation, gender identity or expression, or any other characteristic protected under applicable laws.
This commitment applies to all aspects of employment, including recruitment, hiring, placement, promotion, termination, layoff, recall, transfer, leave, compensation, and training.
If you require a disability\-related or religious accommodation during the application or recruitment process, and wish to discuss possible adjustments, please contact [email protected].
Click here to review InvoiceCloud's Job Applicant Privacy Policy.
For recruitment agencies: InvoiceCloud does not accept unsolicited resumes from agencies. Please do not forward resumes to our job aliases, employees, or any other company location. InvoiceCloud is not responsible for any fees associated with unsolicited submissions.
Salary Context
This $160K-$180K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At InvoiceCloud, 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($170K) sits 6% below the category median. Disclosed range: $160K to $180K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
InvoiceCloud AI Hiring
InvoiceCloud has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Boston, MA, US. Compensation range: $180K - $180K.
Location Context
AI roles in Boston pay a median of $215,350 across 442 tracked positions. That's 8% above the national 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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