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About This Role
Job Description Lazard is one of the world’s preeminent financial advisory and asset management firms. Our people and culture make the difference. While global in presence and reach, ours is a close, collaborative community of just over 3,000 professionals. Lazard is a place of continuous knowledge sharing, skill development and relationship building, where professionals grow and succeed together. Our entrepreneurial culture, flat structure and embrace of individual differences, allow creative ideas, original concepts, and unique perspectives to drive our business forward — and for careers to take flight.
AI is the central pillar of Lazard’s 2030 Growth Strategy. Our ambition is to become the leading AI-enabled independent Financial Services firm. This is to say we are interested in AI beyond the scope of operational efficiency. We see AI and Contextual Alpha as two primary factors underpinning our future success and better outcomes for our clients.
In light of this focus, we are looking for a Head of AI Operations to establish and drive the operating framework that enables the effective, scalable, and responsible deployment of artificial intelligence across the firm. This role will be responsible for translating AI strategy into durable operating capabilities, ensuring that AI models and tools are developed, tested, deployed, monitored, catalogued, supported and governed to the highest institutional standards.
AI is the central pillar of Lazard’s 2030 Growth Strategy. Our ambition is to become the leading AI-enabled independent Financial Services firm. This is to say we are interested in AI beyond the scope of operational efficiency. We see AI and Contextual Alpha as two primary factors underpinning our future success and better outcomes for our clients.
In light of this focus, we are looking for an AI Operations Head to establish and drive the operating framework that enables the effective, scalable, and responsible deployment of artificial intelligence across the firm. This role will be responsible for translating AI strategy into durable operating capabilities, ensuring that AI models and tools are developed, tested, deployed, monitored, catalogued, supported and governed to the highest institutional standards.
We’ll trust you to
- Define and own the end‑to‑end AI operational model, from development and evaluation through deployment, monitoring, lifecycle management, and retirement. Establish consistent operational standards across multiple AI initiatives, agents, teams, and business lines
- Oversee production readiness for AI models, agents and applications, including testing, documentation, validation coordination, access control, documentation standards, deployment controls, and performance monitoring
- Establish evaluation practices for generative AI applications, including measures for accuracy, relevance, and alignment with source material, harmful or inappropriate outputs, and performance against business requirements.
- Partner with engineering, data science, and security teams to threat-model and harden generative AI systems, including RAG applications and agents that use external tools or connect to firm data. Address risks such as prompt injection, jailbreaks, sensitive data exposure, unsafe outputs, and unauthorized access.
- Monitor the performance, data quality, usage, and security of AI systems in production; identify changes in behavior and coordinate remediation when needed.
- Implement governance practices for explainability, documentation, human oversight, and accountability, and support internal and external reviews of AI systems and processes.
- Coordinate AI operational matters across technology, business, and control functions, and provide senior management with clear reporting on operational health, risks, and dependencies.
- Build strong working relationships with advisory teams, investment professionals, and senior leaders to ensure solutions are fit for purpose
You’ll need to have
- 10 or more years of experience leading technology operations, platform operations, or data/analytics operations
- Direct experience supporting AI, machine learning, or advanced analytics systems in production environments and the corresponding operational risk, controls, and governance principles
- Proven ability to lead cross‑functional teams and operate effectively in complex organizational settings
- Exceptional communication skills and influencing abilities to build consensus and drive collegial cross-functional collaboration
- Experience designing or leading AI or analytics operating models at scale strongly preferred
- Background in engineering, data science, or technology risk earlier in career
What We Offer We strive to enhance the total health and well-being of our employees through comprehensive, competitive benefits. Our goal is to offer a highly individualized employee experience that enables you to balance your commitments to career, family, and community. When you work for Lazard, you are working for an organization that cares about your unique talents and passions, and will continue to invest in the development of your career.
We expect the base salary range for this role to be approximately $175,000 - $200,000 USD. Various factors contribute to determining the actual base compensation offered, including but not limited to the applicant’s years of relevant experience, career tenure, qualifications, level of education attained, certifications or other professional licenses held, and relevant skills for the role. Base salary is one component of Lazard’s compensation package, which also includes comprehensive benefits and may include incentive compensation.
Representation at Lazard Lazard is an intellectual capital business committed to delivering the best advice and solutions to clients. To achieve these objectives, we focus on attracting, developing and retaining the best talent. We believe that a workforce comprised of people who represent a wide array of backgrounds, experiences and perspectives creates a rich variety of thought that empowers us to challenge conventional wisdom, solve problems creatively and make better decisions.
Lazard was built on the premise that a multicultural firm can best serve a global clientele. As a global firm that has grown organically from local roots in different countries, we have a deep tradition of respecting and appreciating individual differences. Doing so has been core to our success for over 175 years. We are committed to sustaining an environment where every colleague is supported in their professional pursuits, can maximize their individual potential and contribute to our collective success.
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 Lazard, 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.
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.
Lazard AI Hiring
Lazard has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, United States.
Location Context
AI roles in New York pay a median of $221,000 across 3,078 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 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
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