Interested in this AI/ML Engineer role at Thrivent?
Apply Now →About This Role
As a Principal Engineer in AI Security, you will define and lead Thrivent’s strategy for defending against emerging external AI\-powered threats. This role focuses on protecting the enterprise from adversarial AI capabilities, including automated vulnerability discovery, exploitation, and AI\-driven attack campaigns. You will operate at the intersection of cyber defense, artificial intelligence, and enterprise architecture, translating rapidly evolving external threats into practical, scalable protections across the organization. This role requires deep technical expertise, strong systems thinking, and the ability to influence security strategy at the highest levels.
DUTIES \& RESPONSIBILITIES:
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Designing Solutions
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- Apply expert\-level knowledge of AI\-driven threats, cyber defense, and enterprise security architecture to define and drive outcomes that protect the organization from emerging AI\-powered attack vectors.
- Create the enterprise strategy for defending against AI\-enabled threats, including automated vulnerability discovery, exploitation, and adversarial AI techniques.
- Establish architecture for detection, prevention, and response capabilities specific to generative AI threat patterns.
- Define and implement security patterns and guardrails to enable safe and secure enterprise consumption of AI technologies.
- Drive integration of AI threat detection and controls into existing cyber defense tooling and platforms.
- Build threat models focused on generative AI attack patterns and emerging adversarial techniques.
Implementing Solutions
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- Use independent, critical thinking to translate evolving AI threat intelligence into scalable engineering controls and defensive capabilities.
- Lead the development of detection and response mechanisms for AI\-driven attacks across enterprise environments.
- Design and implement telemetry strategies to identify anomalous behavior indicative of AI\-enabled threats.
- Partner with Cyber Defense teams to operationalize detections, response playbooks, and automation for AI\-related incidents.
- Prototype and evaluate defensive applications of AI to enhance detection, response, and security operations.
- Develop architecturally significant components that advance the organization’s ability to defend against AI\-driven adversaries.
Learning and Applying New Techniques
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- Lead research initiatives focused on emerging AI threat capabilities, adversarial AI techniques, and evolving attack methodologies.
- Maintain deep expertise in industry frameworks such as OWASP LLM Top 10 and MITRE ATLAS.
- Continuously evaluate and introduce modern security technologies and approaches to address AI\-era risks.
- Drive adoption of innovative defensive techniques across the organization to stay ahead of threat evolution.
Collaborating Within the Team
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- Provide deep technical expertise in AI security to solve complex, high\-impact problems and remove critical technical roadblocks.
- Partner with product owners and engineering teams to incorporate AI security requirements into technical designs and user stories.
- Act as a technical leader in system design across teams, ensuring AI security considerations are embedded into broader architecture decisions.
- Mentor engineers and elevate AI security capabilities within teams.
Collaborating Across Teams
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- Partner closely with Cyber Defense, IAM, and Application Security teams to integrate AI threat protections across the security ecosystem.
- Promote adherence to enterprise security standards while extending them to address AI\-specific risks.
- Broker design and implementation of AI security controls across product teams to support strategic priorities.
- Drive alignment across teams on detection engineering, telemetry, and response strategies for AI threats.
Collaborating Across the Organization
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- Influence senior leadership on AI risk posture, threat landscape evolution, and required investments in defensive capabilities.
- Provide enterprise\-wide guidance on AI security architecture, ensuring consistent and scalable protection strategies.
- Represent the organization externally on AI security topics when appropriate (industry forums, partnerships, etc.).
- Translate complex AI security risks into actionable insights for both technical and non\-technical stakeholders.
Setting Product/Platform Technology Strategy
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- Define enterprise\-level capabilities required to defend against AI\-powered threats.
- Shape the strategic direction for AI security across cyber defense, detection engineering, and secure AI adoption.
- Provide subject matter expertise to guide platform and security investments related to AI risk mitigation.
Defining Engineering Standards/Patterns
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- Establish and promote engineering standards for AI security, including secure AI usage patterns and detection frameworks.
- Collaborate across teams to ensure consistent application of AI security controls and design patterns.
- Drive adoption of best practices for integrating AI threat protections into engineering workflows and platforms.
DevOps
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- Provide technical leadership in identifying and responding to AI\-driven security incidents and emerging threats.
- Introduce resilient and scalable technologies to improve detection and response capabilities for evolving attack patterns.
- Evaluate and implement enhancements to CI/CD and operational pipelines to incorporate AI security controls.
- Influence cross\-functional teams to proactively address risks associated with AI\-enabled development and deployment.
Selecting and Managing Technology Vendors
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- Provide technical expertise in evaluating AI security tools, detection platforms, and emerging defensive technologies.
- Assess how vendor solutions align with enterprise strategy for defending against AI\-driven threats.
- Contribute to selection criteria for platforms that enhance AI security posture and detection capabilities.
Coaching Engineers
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- Mentor engineers in AI security concepts, threat modeling, and detection engineering.
- Provide guidance on best practices for securing AI\-enabled systems and defending against adversarial AI techniques.
- Deliver training, workshops, and knowledge\-sharing sessions to build AI security expertise across the organization.
Recruiting and Building Talent
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- Engage in the broader AI and security community to strengthen organizational expertise and visibility in AI security.
- Support recruitment efforts to hire engineers with specialized skills in AI security and advanced cyber defense.
- Model Thrivent’s leadership competencies: Model the Way, Rally the Team, and Deliver Outcomes.
- Foster a culture of continuous improvement, innovation, and strong security practices aligned to evolving AI risks.
QUALIFICATIONS \& SKILLS:
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### Required:
- Bachelor's degree in Computer Science, Cybersecurity, or related technical field, or equivalent work experience
- 10\+ years of experience in security engineering or related field
- Proven experience defining and executing enterprise security strategy
- Deep knowledge of modern threats, attack techniques, and detection engineering
- Experience with threat modeling, incident response, and security operations
- Demonstrated ability to influence both technical and executive stakeholders
### Preferred:
- Experience working with AI/ML systems and security implications
- Knowledge of adversarial AI techniques and AI threat modeling
- Experience with XDR, SIEM, and detection engineering practices
- Familiarity with AI security frameworks (e.g., OWASP LLM Top 10, MITRE ATLAS)
- Financial services industry experience
Pay Transparency
Thrivent’s long\-term growth depends on attracting, rewarding, and retaining people who are committed to helping others thrive with purpose. We accomplish this by offering a wide variety of market competitive compensation programs to attract, reward, and retain top talent. The applicable salary or hourly wage range for this full\-time role is $161,436\.00 \- $218,415\.00 per year, which factors in various geographic regions. The base pay actually offered will be determined by a variety of factors including, but not limited to, location, relevant experience, skills, and knowledge, business needs, market demand, and other factors Thrivent deems important.
Thrivent is unique in our commitment to helping people to be wise with money and live balanced and generous lives. That extends to our benefits.
The following benefits may be offered: various bonuses (including, for example, annual or long\-term incentives); medical, dental, and vision insurance; health savings account; flexible spending account; 401k; pension; life and accidental death and dismemberment insurance; disability insurance; supplemental protection insurance; 20 days of Paid Time Off each year; Sick and Safe Time; 10 paid company holidays; Volunteer Time Off; paid parental leave; EAP; well\-being benefits, and other employee benefits. Eligibility for receipt of these benefits is subject to the applicable plan/policy documents. Thrivent’s plans/policies are subject to change at any time at Thrivent’s discretion. *Thrivent provides Equal Employment Opportunity (EEO) without regard to race, religion, color,* *sex, gender identity, sexual orientation,* *pregnancy,* *national origin, age, disability, marital status, citizenship status, military or veteran status,genetic information, or any* *otherstatus* *protected by applicable local, state,* *or federal law. This policy applies to all employees and job applicants.*
*Thrivent is* *committed to providing reasonable accommodation to individuals with disabilities. If you need a reasonable accommodation**,* *please let us know by sending an email to* *[email protected]* *or call* *800\-847\-4836* *and request Human Resources.*
\#Remote
Salary Context
This $161K-$218K range is above the median for AI/ML Engineer roles in our dataset (median: $181K across 1996 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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At Thrivent, 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 $178,940 based on 11,900 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($189K) sits 6% above the category median. Disclosed range: $161K to $218K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Thrivent AI Hiring
Thrivent has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Minneapolis, MN, US. Compensation range: $218K - $218K.
Location Context
Across all AI roles, 16% (613 positions) offer remote work, while 3,187 require on-site attendance. Top AI hiring metros: New York (2,448 roles, $210,000 median); San Francisco (1,990 roles, $253,000 median); Los Angeles (1,686 roles, $189,000 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, 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 $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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