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About This Role
We are seeking an AI Cybersecurity Engineer to serve as a technical security lead and architect interfacing with our company’s various AI initiatives. This strategic role combines deep expertise in artificial intelligence, machine learning, and cybersecurity to design, architect, and lead the development of secure, scalable AI\-driven security platforms that protect our organization against evolving AI\-powered threats.
In this position, you will serve as the technical visionary and hands\-on architect responsible for defining security strategies for AI systems, engaging with cross\-functional engineering teams, mentoring security professionals, and partnering with senior stakeholders across security, technology, risk, and compliance organizations. You will balance cutting\-edge AI/ML engineering with robust cybersecurity leadership to establish security\-by\-design principles across our AI ecosystem while ensuring our defenses evolve at the speed of emerging threats.
What You Will Do:
### Strategic Architecture \& Technical Leadership
- Design and architect enterprise\-grade, secure AI security platforms that protect ML models, training pipelines, inference systems, and AI\-driven applications from sophisticated adversarial attacks.
- Define and drive the technical vision and security roadmap for all AI/ML initiatives across the organization, embedding security into the complete AI lifecycle from development through deployment and monitoring.
- Lead architectural reviews and provide authoritative technical guidance on security architecture patterns, threat models, and risk mitigation strategies for AI systems.
- Establish security standards and frameworks for AI development, incorporating OWASP LLM Top 10, MITRE ATLAS, NIST AI Risk Management Framework, and other industry best practices.
### AI/ML Security Engineering \& Implementation
- Develop security controls for AI model training, validation, deployment, and monitoring including input/output filtering, model integrity validation, and behavioral anomaly detection.
- Implement data security and privacy controls across AI workflows including sensitive data detection, data loss prevention for AI prompts and responses, and confidential computing techniques.
- Build automated security testing frameworks for continuous validation of AI model security posture and detection of adversarial attack patterns.
- Engineer AI\-powered security detection systems leveraging machine learning for threat hunting, anomaly detection, and behavioral analytics.
### Cross\-Functional Collaboration \& Stakeholder Management
- Communicate complex technical concepts to non\-technical executives and business leaders, translating security risks into business impact and strategic recommendations.
- Serve as the technical authority and trusted advisor on AI security matters for senior leadership including CISO and CTO.
### Governance, Risk \& Compliance
- Develop and enforce AI security governance policies, standards, and guidelines that ensure ethical, safe, and compliant use of AI across the enterprise.
- Establish AI model governance frameworks addressing model validation, bias detection, explainability requirements, and audit trails.
- Implement continuous monitoring and observability for AI systems to detect model drift, performance degradation, and security anomalies in real\-time.
What we need from you:
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- Bachelor's degree in Computer Science, Cybersecurity, Information Security, Software Engineering, or related technical field preferred.
- Advanced coursework or specialization in artificial intelligence, machine learning, cryptography, or secure systems design.
- A minimum or 10 years of progressive experience in cybersecurity engineering , with at least 2\+ years focused on AI/ML security, application security, or security architecture.
- Deep expertise in AI/ML security principles including adversarial machine learning, model security, data poisoning detection, and prompt injection defense.
- Expert\-level knowledge of AI/ML frameworks and platforms (TensorFlow, PyTorch, scikit\-learn, Hugging Face) and their security implications.
- Extensive experience with cloud security architectures on AWS, Azure, OCI, or GCP, specifically securing AI/ML workloads in cloud environments.
- Strong proficiency in programming languages including Python (primary), Java, C\#, Go, or similar with emphasis on secure coding practices.
- Proven experience designing and implementing security for LLMs and generative AI systems including RAG architectures, vector databases, and agent frameworks.
- Demonstrated ability to securely integrate AI/ML solutions with existing legacy applications (e.g., ERP, CRM, mainframe, or on\-prem systems) using modern integration patterns (APIs, gateways, middleware, or RPA), while enforcing enterprise security controls such as RBAC, encryption, logging, and compliance with data governance standards.
- Hands\-on expertise with MLOps/MLSecOps toolchains, CI/CD pipelines, containerization (Docker, Kubernetes), and infrastructure\-as\-code.
- Deep understanding of security frameworks and standards: OWASP LLM Top 10, MITRE ATLAS, NIST AI RMF, ISO 27001, SOC 2\.
- Strong knowledge of cryptography, authentication/authorization protocols, zero\-trust architectures, and identity security principles.
- Demonstrated experience as a technical lead or architect.
- Proven track record architecting complex, distributed security systems at enterprise scale with high availability and performance requirements.
- Extensive experience with threat modeling methodologies and risk assessment frameworks specifically adapted for AI systems.
Preferred Qualifications:
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- Certified AI Security Professional (CASP) or equivalent AI security certification
- CISSP (Certified Information Systems Security Professional), CISM (Certified Information Security Manager), or CCSP (Certified Cloud Security Professional)
- Cloud security certifications: AWS Security Specialty, Azure Security Engineer, or GCP Professional Cloud Security Engineer
- AI/ML certifications from recognized providers (Google, AWS, Microsoft, DeepLearning.AI)
- Experience securing AI agents and autonomous systems including understanding of Model Context Protocol (MCP) security
- Familiarity with AI governance frameworks and responsible AI principles
What we would like from you:
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- Exceptional communication skills with ability to articulate complex security and AI concepts to both technical and executive audiences
- Strategic thinking with ability to balance immediate security needs with long\-term architectural vision
- Strong problem\-solving capabilities and critical thinking when examining novel threat patterns and security challenges
- Collaborative mindset with proven ability to influence without authority and build consensus across diverse stakeholders
- Adaptability and continuous learning orientation given the rapidly evolving AI security landscape
SEI’s competitive advantage:
To help you stay energized, engaged and inspired, we offer a wide range of benefits including comprehensive care for your physical and mental well\-being, a strong retirement plan, tuition reimbursement, a hybrid working environment for most roles, support for working parents and flexible Paid Time Off (PTO) so you can relax, recharge and be there for the people you care about.
Benefits include healthcare (medical, dental, vision, prescription, wellness, EAP, FSA), life and disability insurance (premiums paid for base coverage), 401(k) match, education assistance, commuter benefits, up to 11 paid holidays/year, 21 days PTO/year pro\-rated for new hires which increases over time, paid parental leave, back\-up childcare arrangements, paid volunteer days, a discounted stock purchase plan, investment options, access to thriving employee networks and more.
We are a technology and asset management company delivering on our promise of building brave futures (SM)—for our clients, our communities, and ourselves. Come build your brave future at SEI.
SEI is an Equal Opportunity Employer and so much more…
After over 50 years in business, SEI remains a leading global provider of investment processing, investment management, and investment operations solutions. Reflecting our experience within financial services and financial technology our offices encompass an open floor plan and numerous art installations designed to encourage innovation and creativity in our workforce. We recognize that our people are our most valuable asset and that a healthy, happy, and motivated workforce is key to our continued growth. At SEI, we’re (literally) invested in your success. We offer our employees paid parental leave, back\-up childcare arrangements, paid volunteer days, education assistance and access to thriving employee networks.
SEI is an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability status, protected veteran status, or any other characteristic protected by law.
AI Acceptable Use in the application and interview process:
SEI acknowledges the growing integration of artificial intelligence (AI) tools into individuals’ personal and professional lives. If you intend to incorporate the use of any AI tools at any stage of the application and/or interview process, please ensure you have reviewed and adhere to our AI use guidelines .
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 SEI Investments, 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. Mid-level AI roles across all categories have a median of $200,000.
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.
SEI Investments AI Hiring
SEI Investments has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Oaks, PA, US.
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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