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About This Role
CAIS is the pioneer in democratizing access to and education about alternative investments for independent financial advisors, empowering them to engage and transact with leading asset managers on a massive scale through a wide variety of alternative investment products and technology solutions. CAIS provides financial advisors with a broad selection of alternative investment strategies, including hedge funds, private equity, private credit, real estate, digital assets, and structured notes. CAIS also delivers industry\-leading technology, operational efficiency, and world\-class client service throughout the pre\-trade, trade, and post\-trade experience. CAIS supports over 50,000 advisors who oversee more than $6 trillion in network assets.
CAIS is transforming Platform Operations from a primarily manual function into a technology\-enabled business transformation organization. The AI \& Data Systems Engineer joins the Business Transformation (BT) team as its technical backbone — configuring, integrating, and deploying solutions rapidly without building software from scratch.
BT owns platforms including Snowflake (Cortex, Streamlit), N8N, Airtable, Salesforce, DataHub, Claude/CoWork, and Stonly. You will unblock the team, accelerate automation delivery, and help establish the guardrails needed to scale safely — in close partnership with the Technology organization.
Responsibilities
Platform \& Integration
- Own configuration and integration of BT's tool stack; resolve API, connectivity, and configuration blockers
Design and implement Salesforce* Snowflake integrations and other cross\-system data flows
- Build Streamlit apps and operational dashboards that put data directly in business users' hands
AI \& Automation Enablement
- Deploy intelligent automations using Snowflake Cortex, Claude/CoWork, N8N, and AI\-native tooling
- Co\-pilot BT team members through prototyping, testing, and operationalizing agentic workflows
- Create reusable templates and automation patterns that raise the team's overall delivery velocity
Data \& Reporting Operations
- Maintain Snowflake queries, data models, and reporting layers serving BT's operational and analytical needs
- Use DataHub to catalog assets and support data discoverability across the BT environment
- Assist in ensuring data security and integrity in partnership with Engineering and InfoSec teams
Engineering Practices \& Safe Delivery
- Partner with Technology to implement SDLC, testing, deployment, and version control practices for BT's tool stack
- Define lightweight automation standards (reliability, error handling, logging) — guardrails that enable speed, not slow it
- Maintain documentation so configurations and processes are auditable and transferable
Cross\-Functional Partnership
- Bridge BT and Engineering/InfoSec — translate business requirements into specs, surface constraints early
- Mentor BT team members upskilling in automation and data tools; participate in design reviews with senior engineers
Required
- 2–5 years in systems/automation/data engineering or business systems — fintech preferred
- Hands\-on Snowflake (SQL, queries); Cortex AI or Streamlit a strong plus
- SaaS platform configuration and API integration (Airtable, Salesforce, workflow tools)
- Workflow automation platforms: N8N, Zapier, Make, or equivalent
- Version control (Git) and software engineering fundamentals (environments, testing, CI/CD basics)
- Strong communicator across technical and non\-technical audiences
Preferred
- Salesforce Admin configuration (flows, custom objects, reports); certification a plus
- AI/LLM tooling or agentic frameworks (Claude/CoWork, Langchain, or similar)
- Data cataloging tools: DataHub, Alation, or Collibra
- Python or scripting for automation or data processing
- Streamlit app development or lightweight internal tooling
- Background in alternative investments or wealth management operations
CAIS is consistently recognized as a Best Place to Work, and our culture is at the heart of our success. We are committed to fostering an inclusive environment where employees can be their most authentic selves and feel inspired and supported to bring their voice forward to drive community, growth, and innovation. We are an equal opportunity employer, and do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. Learn more about our culture, benefits, and people at https://www.caisgroup.com/our\-company/careers.
CAIS’ compensation package includes a market competitive salary, a performance bonus, and exceptional benefits. If you are located in New York, New York, the base salary range for this role is $125,000 \- $160,000\. Actual compensation is influenced by a wide array of factors including but not limited to skill set, level of experience, and specific office location.
CAIS offers a comprehensive benefits package that includes generously subsidized healthcare with 100% employer paid dental and vision insurance, an employer matched retirement plan, wellness programs, and generous PTO and parental leave. Additionally, CAIS offers a flexible, hybrid in\-office model; for most roles, we do require a minimum of 3 days in office per week. For more information on our benefits and career opportunities, please visit our website: https://www.caisgroup.com/our\-company/careers.
We use technology, including AI tools, to support parts of our recruitment process such as application screening, interview scheduling, and candidate communications. These tools are used to improve efficiency and consistency, but they do not replace human judgement. All hiring decisions are made by people, and we are committed to fair and unbiased assessment of every candidate.
Salary Context
This $125K-$160K range is below 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 CAPITAL INTEGRATION SYSTEMS, 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. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($142K) sits 35% below the category median. Disclosed range: $125K to $160K.
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.
CAPITAL INTEGRATION SYSTEMS AI Hiring
CAPITAL INTEGRATION SYSTEMS has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $160K - $160K.
Location Context
AI roles in New York pay a median of $220,000 across 1,045 tracked positions.
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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