Interested in this AI/ML Engineer role at Eli Lilly?
Apply Now →Skills & Technologies
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.
Enterprise AI Security Engineer
Role Overview
The Enterprise AI Security Engineer builds, operates, and improves the controls that make AI safe to use at Eli Lilly. Working with the Enterprise AI Security Advisor and senior engineers, this role turns AI security requirements into working guardrails, detections, telemetry, and assessments across commercial AI assistants and coding agents, internally built agents and integrations, models, and AI compute platforms.
The engineer tunes and operates inference-time guardrails, tests AI systems for issues such as prompt injection, sensitive data exposure, excessive permissions, and unsafe agent actions, builds visibility into AI usage and agent activity, and integrates AI security tooling with Lilly's cloud, endpoint, identity, and security operations platforms.
This is a hands-on engineering role in a highly regulated pharmaceutical environment, with direct exposure to how AI is adopted across a large enterprise. The position may be filled at the Sr. Engineer or Principal Engineer level depending on experience.
Key Responsibilities
Guardrail Engineering & Operations
- Implement, tune, and operate detection and policy content for enterprise AI guardrails (for example sensitive data and secrets detection, prompt-injection defenses, and tool-call policies) across AI assistants, coding agents, and internally built agents.
- Triage findings, classify true and false positives against an agreed rubric, and produce the evidence needed to move controls from monitoring to blocking with minimal user impact.
- Maintain runbooks, configuration as code, and test suites for guardrail rules and policies; validate changes before promotion to production.
- Participate in the operational cadence of the service, including regular tuning reviews, change management, and support rotations as needed.
AI Systems Assessment & Testing
- Assess AI applications, agents, integrations (including MCP servers and tool connectors), and third-party AI products to document what they can access and do, and where controls are needed.
- Execute test plans for prompt injection (direct and indirect), sensitive data exposure, excessive permissions, and unsafe agent actions; reproduce issues and write clear, actionable findings.
- Contribute to threat models and security acceptance criteria for AI systems using frameworks such as OWASP Top 10 for LLM Applications and MITRE ATLAS.
- Track remediation with owning teams and verify that fixes are effective.
Visibility, Detection & Response
- Build and maintain telemetry pipelines, analytics, and dashboards for AI usage and agent activity so teams can see where AI is used, what it can access, and whether controls are working.
- Develop detections and alerts for AI-specific events such as data exfiltration through AI tools, anomalous agent behavior, and unsafe tool use, and forward them to SIEM and SOAR platforms.
- Support security operations and insider-threat investigations involving AI tools by providing data, context, and analysis.
- Measure control effectiveness with agreed metrics (coverage, precision, time to detect, user impact) and report trends.
Platform Integration & Automation
- Integrate AI security tooling with cloud (AWS, Azure), endpoint, identity, data protection, and ticketing platforms through governed, authenticated APIs.
- Automate deployment, testing, and rollback of guardrail services and detection content using CI/CD and infrastructure-as-code; keep configurations version-controlled and auditable.
- Evaluate and pilot vendor and platform capabilities under the direction of senior engineers, documenting what they cover and where gaps remain.
- Apply secure engineering practices (least privilege, secrets management, logging) to the tooling the team builds and operates.
Collaboration & Continuous Learning
- Work directly with teams building AI applications and agents to help them apply security requirements and approved patterns.
- Contribute to documentation, standards, and reusable patterns; share findings with security operations, privacy, and platform engineering peers.
- Stay current on LLM and agent threats, vendor guardrail capabilities, and industry frameworks, and bring relevant practices back to the team.
- At the Principal Engineer (R3) level: lead small workstreams end to end, mentor less experienced engineers, and represent the team in cross-functional working sessions.
Basic Qualifications:
- Bachelor's degree in Computer Science, Cybersecurity, Information Systems, or an IT related field.
- 2+ years of experience in security engineering, software engineering, cloud engineering, or security operations.
- Qualified applicants must be authorized to work in the United States on a full-time basis. Lilly will not provide support for or sponsor work authorization or visas for this role, including but not limited to F-1 CPT, F-1 OPT, F-1 STEM OPT,J-1, H-1B, TN, O-1, E-3, H-1B1, or L-1.
Preferred:
- Proficiency in Python and scripting (Bash or PowerShell), with experience using REST APIs and working with structured data (JSON, SQL).
- Working knowledge of cloud platforms (AWS or Azure) and of core security domains such as identity and access management, application security, data protection, or security monitoring.
- Hands-on familiarity with LLM-based applications, coding assistants, or agents, and an understanding of common LLM security issues such as prompt injection, data leakage, and excessive permissions.
- Experience with detection engineering, DLP, or rule tuning in a SIEM, EDR, or proxy/gateway platform.
- Experience building with LLM APIs, agent frameworks, or agent-to-tool protocols (MCP), and with AI evaluation or red-team tooling.
- Familiarity with OWASP Top 10 for LLM Applications and MITRE ATLAS.
- Experience with infrastructure-as-code, containers, and CI/CD pipelines (for example Terraform or CloudFormation, Docker, GitHub Actions).
- Experience querying and analyzing data at scale (SQL, Athena, or similar) and building dashboards.
- Relevant certifications (for example Security+, AWS or Azure security, GIAC) or participation in CTF or AI red-team exercises.
- Ability to analyze data, write clear findings, and communicate technical issues to engineers and non-technical stakeholders.
- Attention to detail, sound judgment, and comfort working in a regulated environment with change control and audit requirements.
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
$66,000 - $173,800
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 $66K-$173K range is in the lower quartile 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 ($119K) sits 43% below the category median. Disclosed range: $66K to $173K.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.