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About This Role
Richemont, one of the world leaders in the luxury sector, has various Houses specializing in jewelry, watches and high-end accessories. Each Maison proudly embodies a tradition of style, quality and craftsmanship and Richemont strives to preserve the heritage and identity specific to each of them. At the same time, we are committed to innovating and designing new products in line with the values of our Houses, through a process of permanent creativity.
At Richemont Americas, we aspire to reflect the ever-changing world around us. We are proud to employ talent from many different backgrounds, experiences, and identities to build a continually evolving, inclusive community where the diversity of our colleagues and clients is rich and celebrated. We believe when inclusion is fully embraced and empowered, creativity and knowledge emerge to deliver excellence, advancing the community that is uniquely Richemont Americas.
AI Cyber Lead
Richemont Group - Cyber| New York, NY
Reports to: Senior Manager Incident Response
Role Overview
The Senior AI Cyber Lead serves as the primary technical and strategic expert and point of reference for AI-driven security capabilities within the CSIRT. This senior individual contributor role drives the design, development, and operationalization of agentic AI solutions across security platforms and processes—from Cortex XSIAM and related cybersecurity tools to broader detection, investigation, and incident response workflows. Acting as the key bridge between the CSIRT and Threat Hunting, Threat Intel, Red Team, Forensics, and Group Security Technology teams, the role ensures AI innovations are securely configured, integrated, scaled effectively, and aligned with enterprise security strategy while elevating the team’s overall AI maturity in complex, scattered environments.
Responsibilities
- Serve as the main technical point of reference and subject-matter expert for all AI development efforts within the CSIRT, partnering with analysts and engineers on best practices for agentic AI, LLM integration, RAG pipelines, secure automation, and AI governance in security operations.
- Lead the design, prototyping, and implementation of AI-powered enhancements spanning Cortex XSIAM (including AI SmartScoring, technique-based incident grouping, and SOAR playbooks) and other security platforms, focusing on automated incidents triage and enrichment, threat hunting, investigation assistance, and response orchestration to deliver measurable reductions in CSIRT KPIs (MTTx).
- Architect and operationalize agentic AI systems and workflows that improve SOC efficiency, reduce mean time to detect and respond, and support complex incident response processes while embedding robust guardrails, human-in-the-loop controls, auditability, least-privilege principles, and secure-by-default patterns aligned with enterprise risk and regulatory requirements based on local markets (e.g. China, Korea).
- Act as the primary liaison and technical bridge between the CSIRT and other teams (including Threat Hunting, Threat Intel, Red Team, Forensics, and Group Security Technology), translating operational needs into AI solutions, aligning roadmaps, and ensuring consistent, secure adoption of AI capabilities across platforms and processes.
- Should own the expertise to identify high-impact AI use cases, conduct threat modeling for AI systems (including prompt injection, tool-use risks, data exposure, autonomous agent behaviors, and identity/security posture risks), and drive continuous improvement of AI-enabled defenses.
- Evaluate emerging market solutions, frameworks, and techniques in agentic AI for SOCs and incident response; pilot promising technologies; and contribute to the definition of secure-by-default patterns, reference architectures, and operational standards for AI in security, including alignment with relevant regulatory frameworks inside the company (AI council).
- Support knowledge sharing, mentorship to SOC analysts, and documentation of agentic developments, safe prompt libraries, evaluation frameworks, and best practices to accelerate responsible AI adoption and elevate AI maturity across the team.
- Generate periodic reports, metrics, and insights on AI capability maturity, performance, and risk posture as needed. Be responsible for governing agents, models, and their interactions, ensuring auditability and alignment with enterprise security and compliance priorities.
- Able to communicate to senior management, executives and C-suite levels at need.
Qualifications
- Bachelor’s degree in Computer Science, Cybersecurity, Information Security, Artificial Intelligence/Machine Learning, Cyber Risk Strategy and Governance, or a related technical field (or equivalent practical experience). Master’s degree preferred.
- Minimum 3–5 years of professional experience in technology or advanced engineering environments (preferably within cybersecurity, security operations, detection engineering, AI security, or AI/ML engineering in enterprise or regulated environments), with demonstrated hands-on work applying AI/ML and agentic workflows to security use cases such as SOC triage, enrichment, and secure AI adoption.
- Strong practical expertise in agentic AI systems, large language models (LLMs), retrieval-augmented generation (RAG), multi-agent orchestration, tool-using agents, and related frameworks. Familiarity with Claude (including Claude Code) or GPT series as LLMs. Experience securing, operationalizing, and governing such systems in production security environments is highly valued.
- Hands-on experience with modern security operations platforms and a strong curiosity and can-do/can-learn mentality, ideally leveraging Cortex XSIAM / Cortex XDR, SIEM, SOAR, XDR, or equivalent tools (such as Microsoft Sentinel, Defender XDR, Purview, and Entra ID), with application of AI features for detection, triage, enrichment, and automated response or the building of agents on these platforms.
- Solid understanding of SOC workflows, incident response processes, threat detection, and security telemetry, combined with knowledge of AI-specific risks (prompt injection, excessive agency, data leakage, model abuse, authorization weaknesses) and mitigation strategies. Familiarity with European and global regulatory frameworks (AI Act, NIS2, DORA, GDPR) and standards (ISO 27001, NIST, CIS) is a plus.
- Proven track record of financial gains resulting in AI implementation on SOC processes.
- Professional competencies: strong analytical and problem-solving skills, ability to translate complex technical risks, architectures, and capabilities into business priorities and actionable roadmaps for technical stakeholders, decision-makers, and C-suite leaders; collaborative mindset; and capacity to influence without formal authority as a senior individual contributor and team reference point.
- Preferred: experience bridging operational security teams (CSIRT/SOC) with broader security technology or platform groups; familiarity with Model Context Protocol (MCP) or similar agent tooling patterns; experience enabling secure, company-wide AI adoption and platform integration (identity, endpoints, cloud, data protection, SIEM, AI governance) in regulated environments; relevant certifications (e.g., related to cybersecurity, cloud, or AI security) are a plus but not mandatory.
- Based in New York; ability to work in a hybrid model as required. Occasional travel may be needed for collaboration with Group teams.
Richemont offers a generous compensation and benefits package for eligible employees. Only candidates selected for further consideration will be contacted.
We Offer – United States
Employee wellbeing is a top priority at Richemont. We offer a comprehensive benefits program to support employees and their loved ones. Our core benefits include medical, dental, and vision programs. Health savings and flexible spending accounts are also available. The company offers income protection solutions including life insurance, disability benefits, and 401(k) with employer match. Understanding the importance of work-life balance, our total rewards include paid time off, a wellness reimbursement benefit, and access to the employee assistance program. Employees are encouraged to make a difference in their local communities with volunteer days off, supporting initiatives that drive change.
At Richemont, We Craft the Future!
Expected Salary Range: $155,000 – 170,000
Salary will be determined based on relevant skills and experience.
#Richemont #WeCraftTheFuture
Salary Context
This $155K-$170K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Richemont, 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 $212,000 based on 11,866 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($162K) sits 23% below the category median. Disclosed range: $155K to $170K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Richemont AI Hiring
Richemont has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $170K - $170K.
Location Context
AI roles in New York pay a median of $221,000 across 2,878 tracked positions. That's 4% above the national 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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