Enterprise AI Security Engineer

Eli Lilly and Company • Us, IN

1d • $66,000 - $158,400 • Onsite

About The Position

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.

Requirements

• 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.
• 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.

Responsibilities

• 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.
• 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.
• 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.
• 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.
• 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.

Benefits

• company bonus (depending, in part, on company and individual performance)
• 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
• well-being benefits (e.g., employee assistance program, fitness benefits, and employee clubs and activities)

What This Job Offers

• Job Type: Full-time
• Career Level: Senior