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About This Role
At Lilly, the work is demanding because patients are waiting. We unite caring with discovery to help make life better for people around the world, knowing that every decision, every detail, and every day matters. Headquartered in Indianapolis, Indiana, our over 50,000 employees around the globe take on complex challenges to discover and deliver life-changing medicines, strengthen how health is understood and managed, and support the communities we serve. This is hard, urgent, selfless work—but it’s work worth doing. If you’re driven by purpose and ready to bring your best to work that truly matters for patients, we invite you to join us.
Role Overview
The Enterprise AI Security Advisor leads how Eli Lilly secures AI systems across the enterprise. Lilly teams use a mix of commercial AI assistants and coding agents, internally built agents and integrations, internal and third-party models, and company-managed AI compute. As adoption grows, this role provides a consistent approach to understanding the risks, putting effective controls in place, and helping teams deploy AI safely.
The Advisor owns the strategy, security requirements, control architecture, tooling, and roadmap for enterprise AI security within Lilly's Cybersecurity organization.
This is a hands-on senior technical leadership role: the Advisor assesses what exists today, identifies the gaps that matter most, evaluates available products, and works with engineering, platform, and security operations teams to implement and operate solutions. The role also shapes security requirements for AI systems built within Lilly and advises leadership on emerging AI risks, practical trade-offs, and where investment will have the greatest impact, all within a highly regulated pharmaceutical environment.
The position may be filled at the Advisor or Sr. Advisor level depending on experience and scope of accountability.
Key Responsibilities
AI Security Strategy & Roadmap
- Establish and own a companywide approach to securing AI agents, models, applications, and the infrastructure that supports them, aligned with Lilly's cybersecurity strategy, policies, and regulatory obligations.
- Maintain a prioritized view of AI security risk across commercial AI products, internally built agents, internal models, and AI compute platforms, and use it to set priorities for the team and its partners.
- Own the roadmap for AI security controls, tooling, and improvements; sequence work by risk reduction, cost, and user impact; report progress and measurable outcomes to leadership.
- Advise senior leaders on emerging AI threats, the practical trade-offs between safety and productivity, and where investment will have the greatest effect.
Security Requirements & Reference Architecture
- Define security requirements and approved patterns for AI systems covering agent identity, permissions and tool access, data access and classification boundaries, human approval and kill-switch mechanisms, logging and monitoring, and incident response.
- Publish reference architectures and acceptance criteria that teams building AI applications, agents, and integrations (including agent-to-tool protocols such as MCP) can apply without a bespoke review each time.
- Set control expectations for inference-time guardrails (prompt-injection defense, sensitive-data detection and blocking, tool-call policy), AI gateways, and model and agent registries.
- Integrate AI security requirements into Lilly's existing security architecture review, risk acceptance, and change management processes.
Assessment, Testing & Assurance
- Assess internally built AI systems and third-party AI products to understand how they are used, what data and systems they can access, what actions they can take, and where controls are needed.
- Develop and lead an approach to testing AI systems for prompt injection (direct and indirect), sensitive data exposure, excessive permissions, unsafe or unintended agent actions, and jailbreak or misuse scenarios, including adversarial red-team exercises.
- Define the evidence required before an AI control moves from monitoring to blocking, including false-positive tolerances, user impact, and rollback plans.
- Maintain threat models for high-value AI systems using frameworks such as OWASP Top 10 for LLM Applications and MITRE ATLAS.
Guardrail Platforms, Tooling & Visibility
- Evaluate, select, and tune security capabilities from existing platforms and vendors, including cloud, endpoint, identity, data protection, and AI-specific security tools, distinguishing capabilities that address Lilly's actual risks from features that do not.
- Establish visibility into AI usage and agent activity across the enterprise (who is using which AI systems, what they can access, and what actions agents take) so teams can investigate issues and measure whether controls are working.
- Lead the design and operation of enterprise AI guardrail services, including detection content, policy tuning, telemetry, dashboards, and integration with SIEM, SOAR, EDR, identity, and ticketing platforms.
- Define detection, alerting, and incident response playbooks for AI-specific events such as data exfiltration through AI tools, compromised or misbehaving agents, and unsafe tool use.
Partnership, Governance & Knowledge Sharing
- Partner with teams building AI applications and agents to incorporate security into their design, build, and deployment processes, and give them a clear, supported path to deploy AI securely.
- Collaborate with data privacy, legal, compliance, quality, and AI governance functions so that AI security controls meet regulatory expectations for auditability, explainability, and high-risk AI classifications.
- Mentor engineers and security operations personnel on AI threat models, safe agent design patterns, and responsible AI principles in cybersecurity contexts.
- Engage with vendors, industry groups, and technology partners to evaluate emerging AI security capabilities and bring proven practices back into Lilly's standards.
- At the Sr. Advisor level: serve as Lilly's enterprise authority on AI security, own the multi-year strategy and investment case, represent Cybersecurity with executive leadership, auditors, and external partners, and set technical direction for other advisors and engineers working on AI security.
What Success Looks Like
In the first year, Lilly has a clear inventory of its most important AI systems and agent capabilities, a prioritized view of their risks, and a practical set of controls that teams can apply. Security can see where agents operate, understand what they can access and do, and respond when something goes wrong. Teams building AI have a clear path to deploy it securely, supported by tools and guidance that fit how they work, and leadership has measurable evidence that AI security controls are working.
Basic Qualifications
- Bachelor's degree in Computer Science, Cybersecurity, Information Systems, or a related IT technical field.
- 10+ years of experience in cybersecurity, security architecture, or platform/software engineering, including experience in a technical leadership, architecture, or senior advisory capacity; a track record of setting technical direction at enterprise scale and influencing executive investment decisions.
Preferred
- Experience designing or leading security programs across multiple products, teams, or business units, and translating broad security goals into technical requirements, an actionable roadmap, and measurable outcomes.
- Strong understanding of cloud security, application security, identity and access management, data protection, and security operations.
- Hands-on experience with AI applications, agents, model platforms, or the infrastructure used to run them, including LLM APIs, agent frameworks, or agent-to-tool integration protocols.
- Strong understanding of LLM-specific threat models, including prompt injection (direct and indirect), jailbreaks, sensitive data leakage, tool misuse and excessive agency, data poisoning, and model misuse; familiarity with OWASP Top 10 for LLM Applications and MITRE ATLAS.
- Ability to evaluate security products and distinguish useful capabilities from features that do not address the organization's actual risks.
- Proficiency in Python or a comparable language for prototyping, analysis, and automation; working knowledge of cloud platforms (AWS or Azure), REST APIs, and infrastructure-as-code.
- Ability to work across engineering, infrastructure, security, product, legal, and leadership teams, with excellent written and verbal communication and the ability to explain AI risk to both technical and business audiences.
- Experience building or operating inference-time guardrails, AI gateways, DLP for AI traffic, or AI detection and response tooling at enterprise scale.
- Experience with AI security reviews, adversarial red-teaming, or AI governance frameworks in regulated industries such as pharmaceuticals or healthcare.
- Experience securing GPU/HPC, model-training, or inference platforms and the data pipelines that feed them.
- Familiarity with AI regulatory expectations, including high-risk AI classifications, auditability requirements, and conformity assessments.
- Experience with SIEM/SOAR, EDR, and identity platforms in a detection engineering or security operations context.
- Relevant certifications (for example CISSP, CCSP, or cloud security certifications) and experience with Agile delivery in cross-functional teams.
Lilly is dedicated to helping individuals with disabilities to actively engage in the workforce, ensuring equal opportunities when vying for positions. If you require accommodation to submit a resume for a position at Lilly, please complete the accommodation request form (https://careers.lilly.com/us/en/workplace-accommodation) for further assistance. Please note this is for individuals to request an accommodation as part of the application process and any other correspondence will not receive a response.
Lilly is proud to be an EEO Employer and does not discriminate on the basis of age, race, color, religion, gender identity, sex, gender expression, sexual orientation, genetic information, ancestry, national origin, protected veteran status, disability, or any other legally protected status.
Our employee resource groups (ERGs) offer strong support networks for their members and are open to all employees. Our current groups include: Africa, Middle East, Central Asia (AMECA), Black Employees at Lilly (BE@Lilly), Chinese Culture Network (CCN), EnAble, Evolve, Lilly Indian Network (LIN), Organization of Latinx at Lilly (OLA), Pride (LGBTQ+ Allies), Veterans Leadership Network (VLN) and Women’s Initiative for Leading at Lilly (WILL).
Actual compensation will depend on a candidate’s education, experience, skills, and geographic location. The anticipated wage for this position is
$129,000 - $253,000
Full-time equivalent employees also will be eligible for a company bonus (depending, in part, on company and individual performance). In addition, Lilly offers a comprehensive benefit program to eligible employees, including eligibility to participate in a company-sponsored 401(k); pension; vacation benefits; eligibility for medical, dental, vision and prescription drug benefits; flexible benefits (e.g., healthcare and/or dependent day care flexible spending accounts); life insurance and death benefits; certain time off and leave of absence benefits; and well-being benefits (e.g., employee assistance program, fitness benefits, and employee clubs and activities).Lilly reserves the right to amend, modify, or terminate its compensation and benefit programs in its sole discretion and Lilly’s compensation practices and guidelines will apply regarding the details of any promotion or transfer of Lilly employees.
#WeAreLilly
Salary Context
This $129K-$253K range is above the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Eli Lilly, 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($191K) sits 10% below the category median. Disclosed range: $129K to $253K.
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.
Eli Lilly AI Hiring
Eli Lilly has 7 open AI roles right now. They're hiring across AI/ML Engineer, Research Scientist. Positions span Indianapolis, IN, US, IN, US, San Diego, CA, US. Compensation range: $173K - $391K.
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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