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About This Role
Lead Architect, GenAI Platform \& Agentic AI
About the Role
As a Lead GenAI Platform Architect on the AI Platform \& Infrastructure Engineering team, you will be responsible for the end\-to\-end architecture of the firm's Generative AI platform, while staying deeply hands\-on in the design, execution, and delivery of GenAI initiatives. You will set technical directions for the firm's GenAI platform while also building the first reference implementations that other engineering teams will adopt. You will define the evolving GenAI reference architecture, codify secure delivery patterns, write reusable infrastructure constructs, design IAM and deployment role chains, provision agent runtimes and data layers, and resolve the platform\-level issues.
You will build a strong understanding of the firm's existing technology platform, identify architectural and control gaps, and establish the secure paved road for GenAI delivery: reusable infrastructure patterns, guardrails, runbooks, operational standards, and reference implementations that allow teams to move faster without compromising security, resilience, auditability, or regulatory expectations. You will serve as a key technical authority for GenAI platform infrastructure, help define the firm's Enterprise Architecture for GenAI in collaboration with application engineering, Information Security and platform operations to define secure, scalable, and enterprise\-ready delivery patterns.
Job Responsibilities
- Contribute and own the successful delivery of the firm's GenAI priorities.
- Own and evolve the reference cloud architecture for the firm's GenAI platform on AWS, including Amazon Bedrock, AgentCore runtimes, agent gateways, shared data layers, and secure integration patterns.
- Build reusable reference implementations using AWS CDK and infrastructure\-as\-code patterns so engineering teams can adopt approved GenAI capabilities consistently and securely.
- Design and implement secure multi\-account, multi\-environment, and multi\-repo deployment patterns, including least\-privilege IAM, OIDC federation, role chains, permission boundaries, and clear separation of deployment and runtime roles in partnership with Infrastructure teams.
- Provision and define operating patterns for platform data layers, including graph databases, vector search capabilities, Redis, semantic caching, secure access, backup/restore, scaling, and performance tuning.
- Partner with Infrastructure teams and Information Security to define guardrails, audit logging, governance standards, and security\-review patterns for AI and LLM workloads.
- Own and improve CI/CD, container deployment, networking, observability, rollback, drift detection, and operational validation patterns across GenAI platform components.
- Troubleshoot complex platform issues, produce runbooks and design guidance, and mentor senior engineers through architecture, security, and operational design reviews.
Required Qualifications
- 10\+ years of experience in platform engineering, cloud infrastructure, DevOps, or platform ownership, including experience in a principal, staff, lead architect, or equivalent hands\-on technical leadership role.
- Deep hands\-on AWS experience across IAM, CloudFormation, Lambda, API Gateway, CloudWatch, SSM, Secrets Manager, ECR, VPC, Cognito, and related enterprise platform services.
- Strong experience with AWS CDK or equivalent infrastructure\-as\-code frameworks, including reusable constructs, shared bootstrapping, multi\-account deployments, and multi\-environment delivery patterns.
- Expertise in IAM least privilege and CI/CD security, including OIDC federation, short\-lived credentials, role assumption, trust policies, permission boundaries, pass\-role controls, and deployment/runtime role separation.
- Understanding of enterprise authentication and authorization patterns using Okta, including OIDC/OAuth 2\.0 and SAML federation, MFA, SSO, group\-to\-role mapping, token validation, and integration with AWS IAM Identity Center, Cognito, API Gateway, and GenAI agent runtimes.
- Production experience designing and operating secure cloud platforms with stateful data layers such as graph databases, vector stores, Redis, OpenSearch, or equivalent systems.
- Ability to assess an existing platform, identify architectural and security gaps, deliver practical improvements with limited oversight, and communicate effectively across engineering, architecture, security, and business stakeholders.
Preferred Qualifications
- Hands\-on experience with Amazon Bedrock, Amazon Bedrock AgentCore, or comparable GenAI agent orchestration and runtime platforms.
- Experience securing GenAI platforms, including agent\-to\-agent communication, service\-to\-service authentication, tool access, runtime isolation, and API\-driven LLM systems.
- Experience integrating graph databases, vector stores, and Redis\-like systems into GenAI architectures for knowledge graphs, retrieval, semantic caching, session memory, or rate limiting.
- Experience with enterprise guardrails, audit logging, AI governance, model risk, evaluation, and security\-review processes for AI workloads in regulated environments.
- Familiarity with private subnet architectures, VPC endpoints, AWS PrivateLink, OpenTelemetry, CloudWatch, and network\-aware deployment patterns for Lambda, Bedrock, data stores, and containerized GenAI runtimes.
The base salary range for this position is $170,000 \- $220,000 per year. This range reflects the minimum and maximum base salary we reasonably expect to pay for this role. In addition, this position may be eligible to participate in the relevant business unit's incentive compensation plan, and other compensation programs as applicable. Eligible employees may participate in a 401(k) program with a generous profit\-sharing contribution, medical, prescription dental, and vision coverage; life insurance; disability coverage; paid holidays; vacation; and sick time, subject to plan terms and Company policies.
About Bessemer Trust:
- Bessemer Trust is a family office, overseeing $250 billion in assets for 3,000 individuals and families of substantial wealth. Its more than 1,300 employees are singularly focused on private wealth management — disciplined investment management, sophisticated wealth planning, comprehensive family office services, and highly personalized client service.
- Established in 1907 as the family office for Annie and Henry Phipps, Bessemer Trust is in its seventh generation of ownership by the Phipps family. As a self\-made entrepreneur, Henry Phipps was a founding partner and chief financial officer of Carnegie Steel.
- Bessemer Trust retains its original focus as a privately owned and independent wealth manager deeply committed to its mission of providing peace of mind to its clients. Bessemer's adherence to putting clients' interests first, fiduciary mindset, and highly collaborative culture are at the heart of everything the firm does.
Key Facts:
- For more than 119 years, Bessemer Trust has operated continuously in a single line of business, independently owned by one family.
- Headquartered in New York's Rockefeller Center, Bessemer Trust has 22 offices in total. Woodbridge, NJ, is one of the firm's largest offices, which hosts a wide range of technology and operations professionals. In addition to its sizable presence in New York and Woodbridge, the firm provides client service through offices in Atlanta, Boston, Chicago, Dallas, Delaware, Denver, Garden City, Grand Cayman, Greenwich, Houston, Los Angeles, Miami, Naples, Nevada, Palm Beach, San Diego, San Francisco, Seattle, Stuart, and Washington, D.C.
- To watch a video about Bessemer Trust's history, click here.
- To learn more about Bessemer Trust, click here.
About Our Employee Rewards and Benefits:
- We provide exceptional rewards and benefits that are among the best in the industry, giving our people access to a wide range of options, including:
- Competitive base salary plus discretionary annual bonus for select positions
- A 401(k) plan with a generous annual profit\-sharing contribution
- Personalized development and career opportunities, including tuition reimbursement support
- Comprehensive medical, dental, and vision plans with zero contributions for employee coverage
- Employee assistance (EAP) and wellness programs
- Hybrid work environment: 60% in office, 40% remote for most positions
- Paid time off and paid parental leave
- Employer\-paid life insurance and short\- and long\-term disability coverage
- Legal services and financial wellness plans at no cost to employees
*Bessemer Trust is committed to creating a diverse and inclusive environment and is proud to be an equal opportunity employer. We encourage candidates of diverse backgrounds to apply.*
Salary Context
This $170K-$220K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Bessemer Trust, 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($195K) sits 11% below the category median. Disclosed range: $170K to $220K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Bessemer Trust AI Hiring
Bessemer Trust has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Woodbridge, NJ, US. Compensation range: $220K - $220K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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