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About This Role
Requisition ID: 50718 Business Unit: Fitch Group Category: Information Technology Location: New York, NY, US Date Posted: Oct 6, 2026 Fitch Group is currently seeking a Cloud AI Platform Architect (Anthropic and OpenAI) to join our Cloud Infrastructure & Platform Engineering team in New York
As a leading, global financial information services provider, Fitch Group delivers vital credit and risk insights, robust data, and dynamic tools to champion more efficient, transparent financial markets. With over 100 years of experience and colleagues in over 30 countries, Fitch Group’s culture of credibility, independence, and transparency is embedded throughout its structure, which includes Fitch Ratings, one of the world’s top three credit ratings agencies, and Fitch Solutions, a leading provider of insights, data and analytics. With dual headquarters in London and New York, Fitch Group is owned by Hearst.
At Fitch Group, the combined power of our global perspectives is what differentiates us. It is the strength of our business. It comes from people around the world in a shared pursuit: to equal something greater than they could ever accomplish alone. Every team member is essential to our business and every perspective is critical for our success. Develop yourself in a culture that values flexibility, expects your opinion and trusts your decisions. Together we evolve and so will you, in an environment that asks you to challenge yourself – and us.
About the Team
This is a leadership role within Cloud Infrastructure & Platform Engineering, serving as subject matter expert in Enterprise AI Deployment. This team owns Core DevOps Tools, Cloud Platform Engineering, Networking, and Reliability Engineering for all Fitch Group businesses. The role is responsible for the enterprise configuration, and operationalization of machine learning and large‑language‑model platforms, with a primary focus on platforms such as Anthropic (Claude), OpenAI, Databricks, and Snowflake. This role owns how these platforms are securely provisioned, governed, monitored, and scaled across multiple organizations, environments, and use cases.
How You’ll Make an Impact:
- Sitting at the intersection of DevOps, emerging technology, technical operations, and information security, this role ensures that foundation-model platforms are production‑ready: highly available, cost‑controlled, compliant, and easy for engineering and business teams to consume safely.
- The role is not focused on building applications or prompts, but on designing and running the underlying AI platforms that everything else depends on. They partner with Core Engineering and Reliability Engineering squads to integrate AI platforms into the larger Cloud ecosystem at Fitch.
- Enable self‑service access for approved teams, with guardrails that prevent misconfiguration or policy violations.
- Evaluate new capabilities from Anthropic and OpenAI (e.g., advanced agent features, new models, tooling changes) and determine enterprise readiness. Enable safe experimentation with these as well as competing services.
- Influence vendor roadmap discussions by bringing an enterprise platform perspective.
- Establish guardrails for cost management and forecasting, working with TechOps and Finance where needed.
- Partner closely with Information Security and Technology Operations to embed security, privacy, and data controls into AI platform configurations.
- Implement and enforce: Role‑based access and segregation of duties, Data handling and retention controls, Model usage boundaries and red‑line capabilities
- Support auditability and compliance requirements by ensuring AI usage is logged, traceable, and reviewable.
- Serve as the technical owner for translating policy into enforceable platform controls.
- Establish Infrastructure‑as‑Code and configuration‑as‑code approaches for AI platform provisioning where possible.
- Partner with SRE and TechOps teams to ensure platform reliability, scalability, and operational maturity.
You May be a Good Fit if:
- You have hands-on experience deploying or operating Anthropic and/or OpenAI in enterprise or production environments.
- Experience providing AI services via Azure, AWS, and/or Google Cloud
- Strong background in DevOps, platform engineering, or cloud infrastructure, with an understanding of how to productionize developer platforms.
- Experience partnering with Information Security on access controls, governance, and risk management.
- Practical experience with API platforms, environment separation, and multi‑tenant or multi‑org designs.
- Ability to balance innovation speed with operational discipline and safety.
- Strong written communication skills for documenting platform standards, guardrails, and operating models.
What Would Make You Stand Out:
- Experience supporting AI platforms in regulated or risk‑sensitive environments.
- Familiarity with agentic AI patterns, orchestration layers, or AI platform governance models.
- Background providing services to large organizations with multiple business units
- A track record of creating architecture diagrams, design documentation, standards, runbooks, troubleshooting guides, and knowledge-transfer materials that improve supportability and reduce single points of expertise.
Why Choose Fitch:
- Hybrid Work Environment: 2 to 3 days a week in office required based on your line of business and location
- A Culture of Learning & Mobility: Dedicated trainings, leadership development and mentorship programs designed to ensure that your time at Fitch will be a continuous learning opportunity
- Investing in Your Future: Retirement planning and tuition reimbursement programs that empower you to achieve your short and long-term goals
- Promoting Health & Wellbeing: Comprehensive healthcare offerings that enable physical, mental, financial, social, and occupational wellbeing
- Supportive Parenting Policies: Family-friendly policies, including a generous global parental leave plan, designed to help you balance career and family life effectively
- Inclusive Work Environment: A collaborative workplace where all voices are valued, with Employee Resource Groups that unite and empower our colleagues around the globe
- Dedication to Giving Back: Paid volunteer days, matched funding for donations and ample opportunities to volunteer in your community
Fitch is committed to providing global securities markets with objective, timely, independent and forward-looking credit opinions. To protect Fitch’s credibility and reputation, our employees must take every precaution to avoid conflicts of interest or any appearance of a conflict of interest. Should you be successful in the recruitment process at Fitch Ratings you will be asked to declare any securities holdings and other potential conflicts prior to commencing employment. If you, or your immediate family, have any holdings that may conflict with your work responsibilities, you may be asked to divest yourself of them before beginning work.
Fitch is proud to be an Equal Opportunity and Affirmative Action Employer. We evaluate qualified applicants without regard to race, color, national origin, religion, sex, sexual orientation, gender identity, disability, protected veteran status, and other statuses protected by law.
FOR NEW YORK ROLES ONLY: Expected base pay rates for the role will be between $200,000 and $220,000 per year. Actual salaries will be determined on an individualized basis and may vary based on factors including but not limited to education, training, experience, past performance, and other job-related factors. Base pay is one part of Fitch’s total compensation package, which, depending on the position, may also include commission earnings, discretionary bonuses, long-term incentives, and other benefits sponsored by Fitch
Salary Context
This $200K-$220K 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 Fitch Group, 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. Director-level AI roles across all categories have a median of $272,300. Disclosed range: $200K to $220K.
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.
Fitch Group AI Hiring
Fitch Group has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $220K - $220K.
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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