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Lead AI/Agentic Identity Engineer
Req number:
R8266
Employment type:
Full time
Worksite flexibility:
Remote
Who we are
--------------
CAI is a global services firm with over 9,000 associates worldwide and a yearly revenue of $1\.3 billion\+. We have over 40 years of excellence in uniting talent and technology to power the possible for our clients, colleagues, and communities. As a privately held company, we have the freedom and focus to do what is right—whatever it takes. Our tailor\-made solutions create lasting results across the public and commercial sectors, and we are trailblazers in bringing neurodiversity to the enterprise.
Job Summary
We are looking for a motivated Lead AI/Agentic Identity Engineer ready to take us to the next level! If you have deep expertise in identity architecture, AI agent security, OAuth/OIDC, workload identity, and Zero Trust principles, and are looking for your next career move, apply now.
Job Description
We are looking for a Lead AI/Agentic Identity Engineer to lead the design and implementation of an enterprise identity architecture for AI agents, autonomous workflows, and other non\-human identities. The Lead AI/Agentic Identity Engineer will establish enterprise standards, governance models, and implementation blueprints that enable AI agents to operate as secure, auditable, and governed digital identities across cloud, hybrid, internal, and guest\-facing environments. This position will be a contract and remote with occasional travel to Miami, FL.
Due to the specific legal and contractual requirements associated with this position, this role will be direct employment with CAI. This position does not offer work authorization sponsorship now or in the future.
What You'll Do
- Define the enterprise target\-state architecture for AI agent identity, authorization, governance, and auditability
- Design operational models for governed non\-human identities, including ownership, lifecycle management, registration, approval, attestation, recertification, and retirement
- Create reusable reference architectures for agent onboarding, delegated access, tool invocation, runtime policy enforcement, and attribution
- Lead architecture design across identity providers, CI/CD pipelines, agent platforms, secrets management systems, API gateways, service meshes, and cloud\-native workload identity services
- Develop implementation blueprints for cryptographic workload identity, including SPIFFE/SPIRE, mTLS, certificate\-based authentication, short\-lived credentials, and key lifecycle management
- Define authorization frameworks utilizing OAuth 2\.0/2\.1, OIDC, token exchange, delegated authority models, audience\-bound tokens, and just\-in\-time access controls
- Establish architecture standards for Model Context Protocol (MCP), Agent2Agent (A2A), multi\-agent orchestration, and secure tool\-calling systems
- Design policy decision and enforcement models across APIs, services, workloads, and AI runtimes
- Guide implementation of runtime guardrails, including human\-in\-the\-loop approvals, step\-up authentication, revocation controls, rate limiting, transaction thresholds, and emergency stop capabilities
- Partner with security, infrastructure, platform, and application teams to align AI identity controls with Zero Trust, privileged access management, secrets management, vulnerability management, and detection engineering programs
- Define telemetry, logging, audit, and traceability requirements across user, agent, sub\-agent, tool, and data access interactions
- Lead architecture reviews, threat modeling exercises, design workshops, and implementation planning sessions
- Develop executive\-facing roadmaps, architecture decision records, implementation guidance, control mappings, and knowledge transfer materials
What You'll Need
Required:
- 10\+ years of experience in enterprise security architecture, IAM architecture, cloud security, platform security, or application security
- Proven experience designing and implementing enterprise IAM, workload identity, or non\-human identity solutions at scale
- Deep expertise in identity providers, OAuth/OIDC, service\-to\-service authentication, token security, API security, and cloud authorization frameworks
- Strong knowledge of Zero Trust architecture, least privilege principles, privileged access management, identity governance, access certification, and policy\-based access control
- Experience designing secure architectures for distributed systems, APIs, microservices, containers, service meshes, and hybrid or multi\-cloud environments
- Ability to design deterministic security controls for autonomous and AI\-enabled systems
- Familiarity with agentic AI security concepts, AI agent runtimes, delegated authority, tool\-calling frameworks, and AI\-specific risks such as prompt injection, excessive agency, unsafe tool usage, and autonomous process escalation
- Experience leading cross\-functional architecture workshops and translating business, security, risk, and compliance requirements into technical solutions
- Strong written and verbal communication skills with experience producing architecture documentation, design standards, implementation guides, executive summaries, and governance artifacts
Preferred:
- Experience with AI agent frameworks, orchestration platforms, MCP, A2A, or emerging agent identity standards
- Experience with SPIFFE/SPIRE, workload identity federation, service mesh security, mTLS, PKI, and certificate lifecycle management
- Experience implementing policy\-as\-code solutions, runtime authorization frameworks, ABAC/ReBAC models, or centralized policy decision points
- Experience designing security controls for highly regulated, privacy\-sensitive, SOX, PCI, or high\-availability environments
- Experience developing security maturity models, capability roadmaps, governance frameworks, and executive investment strategies for emerging technologies
Physical Demands
- Ability to safely and successfully perform the essential job functions consistent with the ADA and other federal, state, and local standards
- Sedentary work that involves sitting or remaining stationary most of the time with occasional need to move around the office to attend meetings, etc.
- Ability to conduct repetitive tasks on a computer, utilizing a mouse, keyboard, and monitor
Reasonable accommodation statement
If you require a reasonable accommodation in completing this application, interviewing, completing any pre\-employment testing, or otherwise participating in the employment selection process, please direct your inquiries to [email protected] or (888\) 824 – 8111\.
EEO Statement
It is the policy of Computer Aid, Inc.(CAI) not to discriminate against any employee or applicant for employment because of race, color, religion, sex, sexual orientation, gender identity, national origin, age, marital status, genetic information, disability or because he or she is a protected veteran. It is also the policy of CAI to take affirmative action to employ and to advance in employment, all persons regardless of race, color, religion, sex, sexual orientation, gender identity, national origin, age, marital status, genetic information, disability or protected veteran status, and to base all employment decisions only on valid job requirements. This policy shall apply to all employment actions, including but not limited to recruitment, hiring, upgrading, promotion, transfer, demotion, layoff, recall, termination, rates of pay or other forms of compensation and selection for training, including apprenticeship, at all levels of employment.
Employees and applicants of CAI will not be subject to harassment on the basis of race, color, religion, sex, sexual orientation, gender identity, national origin, age, marital status, genetic information, disability or because he or she is a protected veteran. Additionally, retaliation, including intimidation, threats, or coercion, because an employee or applicant has objected to discrimination, engaged or may engage in filing a complaint, assisted in a review, investigation, or hearing or have otherwise sought to obtain their legal rights under any Federal, State, or local EEO law is prohibited.
$85\-$90 per hour
The pay range for this position is listed above. Exact compensation may vary based on several factors, including location, experience, and education. Benefit packages include medical, dental, and vision insurance, as well as 401k retirement account access. Employees in this role may also be entitled to paid sick leave and/or other paid time off as provided by applicable law.
Salary Context
This $176K-$187K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At CAI (Computer Aid, Inc.), 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($182K) sits 15% below the category median. Disclosed range: $176K to $187K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
CAI (Computer Aid, Inc.) AI Hiring
CAI (Computer Aid, Inc.) has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US. Compensation range: $187K - $187K.
Location Context
AI roles in Austin pay a median of $214,343 across 143 tracked positions.
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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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