Interested in this AI/ML Engineer role at FCT?
Apply Now →Skills & Technologies
About This Role
Who We Are
Join a team that puts its People First! Since 1889, First American (NYSE: FAF) has held an unwavering belief in its people. They are passionate about what they do, and we are equally passionate about fostering an environment where all feel welcome, supported, and empowered to be innovative and reach their full potential. Our inclusive, people-first culture has earned our company numerous accolades, including being named to the Fortune 100 Best Companies to Work For® list for eleven consecutive years. We have also earned awards as a best place to work for women, diversity and LGBTQ+ employees, and have been included on more than 50 regional best places to work lists. First American will always strive to be a great place to work, for all. For more information, please visit www.careers.firstam.com.What We Do
Our Security Operations Center (SOC) protects the enterprise by detecting, investigating, and responding to cybersecurity threats.
As a member of our Threat Detection Engineering team operating within our SOC, you will build governed agent capabilities to support data onboarding, detection engineering, threat hunting, investigation, response, automation, and platform operations. Our goal is to expand threat visibility, accelerate security outcomes, reduce repetitive work, and improve operational consistency and resilience without compromising sound security judgment, human accountability, or control.
The Principal Engineer provides hands-on technical leadership to turn this strategy into secure, reliable, and supportable production capabilities across the SOC.
- We are open to remote candidates for this role
What You’ll Do
- Lead the architecture and hands-on implementation of Agentic Security Operations capabilities across the SOC.
- Define and evolve the reference architecture for governed data services, agent capabilities, workload routing, execution environments, and deterministic orchestration.
- Build reusable capabilities that translate operational intent into tested, reviewable, and auditable security outcomes.
- Establish engineering standards for creating, testing, registering, approving, releasing, monitoring, and retiring agents and their capabilities.
- Advance detection-as-code practices, including versioning, simulation, deployment, tuning, migration, and rollback.
- Build governed knowledge and context services that provide authoritative documentation, policies, operational history, citations, freshness information, and permission-aware retrieval.
- Design workload identity, least-privilege access, short-lived authorization, spend controls, approval gates, audit trails, rollback, and emergency shutdown mechanisms.
- Define autonomy levels that determine which activities may execute automatically, require human approval, or remain analyst led.
- Establish agent evaluation and observability through historical replay, synthetic scenarios, expected-versus-actual comparisons, policy tests, traces, outcome verification, and regression testing.
- Measure quality, security, reliability, latency, cost, analyst acceptance, and operational impact.
- Engineer reliable integrations across search, case management, workflow, communications, and response systems, including retries, idempotency, partial failures, and reconciliation.
- Ensure consequential response actions remain deterministic, attributable, reversible, and subject to appropriate human oversight.
- Design production reliability, continuity, recovery, and support models for agent capabilities and security data services.
- Lead proofs of concept and architecture evaluations using measurable criteria for security, fidelity, scale, reliability, portability, operating effort, and economic value.
- Collaborate with enterprise architecture, infrastructure, data, risk, privacy, legal, and compliance teams.
- Mentor engineers, establish technical standards, review designs, and influence Threat Detection Engineering strategy.
- Present complex technical decisions, risks, tradeoffs, and investment recommendations clearly to engineering and executive stakeholders.
- Partner across Threat Detection and Response groups, including engineering, threat hunting, security analysis, investigation, and incident response stakeholders on initiatives and to translate operational feedback into improvements within our agentic harness, evaluations, safeguards, workflows, and other platforms.
What You’ll Bring
- Extensive experience designing and operating enterprise security, software, data, or distributed systems at scale.
- Demonstrated success as a principal-level technical leader while remaining actively involved in architecture, prototyping, coding, testing, and production delivery.
- Hands-on experience delivering production AI-assisted or agentic systems that use tools, memory, retrieval, planning, and structured workflows.
- Strong understanding of security operations, including telemetry management, detection engineering, threat hunting, triage, investigation, response, and automation.
- Experience with high-volume streaming and batch data pipelines, object-based storage, elastic analytics, distributed query, schema management, and data-quality controls.
- Experience designing modular agents, reusable capabilities, workload routing, local and managed execution, and human-in-the-loop workflows.
- Deep understanding of grounding, retrieval, source attribution, context freshness, permission inheritance, and protection against untrusted or manipulated context.
- Experience evaluating nondeterministic systems using versioned test sets, historical replay, synthetic scenarios, regression testing, and measurable outcome criteria.
- Strong software-engineering practices, including source control, automated testing, continuous delivery, infrastructure automation, peer review, and rollback.
- Experience designing secure APIs, event-driven integrations, workload identities, authorization boundaries, secrets management, and auditable action execution.
- Knowledge of AI security risks such as prompt injection, data poisoning, excessive agency, sensitive-data exposure, insecure tool use, and model or workflow drift.
- Strong production-engineering experience covering observability, service objectives, capacity, failure isolation, continuity, recovery, and operational support.
- Ability to evaluate emerging capabilities critically, distinguish demonstrated functionality from product claims, and validate architectural assumptions through controlled testing.
- Excellent written and verbal communication skills, including the ability to document architectures, operating models, engineering standards, and executive recommendations.
- Ability to lead through influence and work effectively across organizational and technical boundaries.
- Hands-on experience with cloud-scale data and analytics platforms supporting security operations, including distributed query, high-volume data processing, object storage, schema management, data pipelines, and the operational patterns required to run security analytics at scale (e.g., Databricks, Snowflake, etc.)
- Experience operating and migrating security analytics and detection workloads across modern platforms, along with strong working knowledge of major public cloud environments (AWS, Azure, GCP) and the identity, networking, logging, data protection, and workload-security controls required to secure them.
- A minimum of 10 years’ experience in cybersecurity or relevant fields.
- Bachelor’s degree in computer science, Information Technology, Cybersecurity, a related field, or equivalent work experience.
- Preferred certifications such as, GASAE, GAIPS, GDSA, GCDA, CEH, OSCP, CISSP, etc.
Preferred Qualifications
- Experience modernizing a centralized security analytics environment into a decoupled architecture with customer-controlled data and replaceable compute or control layers.
- Experience with knowledge graphs, entity resolution, semantic normalization, retrieval-augmented systems, or multi-agent coordination.
- Experience migrating security content while validating semantic equivalence, historical fidelity, and operational parity.
- Background in detection engineering, incident response, threat hunting, adversary emulation, or security automation.
- Experience defining governance and autonomy frameworks for AI-enabled production systems.
- Experience building internal platforms or shared engineering services used by multiple security functions.
What Success Looks Like
- A leadership-approved Agentic Security Operations reference architecture and governance model.
- Governed agent prototypes progressing into reliable production capabilities.
- Reusable capabilities spanning data, detection, automation, and other SOC functions.
- Measurable reductions in engineering effort and operational cycle time without sacrificing security or human accountability.
- Continuous evidence that agent capabilities are accurate, secure, reliable, explainable, cost-effective, and safe to operate.
- A production platform with clear ownership, support procedures, recovery controls, and an intentional path away from vendor or architecture lock-in.
Pay Range: $170,900.00 - $227,900.00 Annually
This hiring range is a reasonable estimate of the base pay range for this position at the time of posting. Pay is based on a number of factors which may include job-related knowledge, skills, experience, business requirements and geographic location. * Note that the following statements only apply to candidates who will be working from an unincorporated area within Los Angeles County. *
First American will consider for employment all qualified applicants, including those with arrest or conviction records, in a manner consistent with the requirements of applicable state and local laws (e.g., the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act).
First American intends to conduct a review of an applicant’s criminal history in connection with a conditional offer. First American reasonably believes that a criminal history may have a direct, adverse and negative relationship with the following material job duties for this position potentially resulting in the withdrawal of the conditional offer of employment: handling of confidential, proprietary or trade secret information belonging to First American or its customers, administrating or facilitating financial transactions, and the ability to meet customer-imposed criminal history requirements.
What We Offer
By choice, we don’t simply accept individuality – we embrace it, we support it, and we thrive on it! Our People First culture is inclusive for all employees - not just because it's the right thing to do, but because it's the key to our success. We are proud to foster an authentic and inclusive workplace For All. You are free and encouraged to bring your entire, unique self to work. First American is an equal opportunity employer in every sense of the term. Based on eligibility, First American offers a comprehensive benefits package including medical, dental, vision, 401k, PTO/paid sick leave and other great benefits like an employee stock purchase plan.
Salary Context
This $170K-$227K 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 FCT, 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. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($199K) sits 6% below the category median. Disclosed range: $170K to $227K.
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.
FCT AI Hiring
FCT has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Santa Ana, CA, US. Compensation range: $227K - $227K.
Remote Work Context
Remote AI roles pay a median of $188,000 across 2,316 positions. About 6% of all AI roles offer remote work.
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.