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About This Role
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Sr. AI Engineer – Asset Management, Investment Data & Reporting
Location: Remote - US / CANADA
Type : Full-time / Contract
About Cutter:
At Cutter Associates, we empower the world’s leading asset managers and asset owners with the intelligence they need to stay ahead. For nearly 30 years, and with a global presence, we’ve combined expert consulting with proprietary research and industry benchmarking to help top-tier investment firms make smarter, faster decisions. Our global team is known for its deep domain expertise, collaborative spirit, and relentless curiosity. As we expand into new markets and capabilities, now is the perfect time to grow your career with a company that values insight, innovation, and impact.
As we expand our reach and deepen our impact, we are seeking an AI Engineer to design and deliver AI-enabled solutions for asset managers, asset owners, and institutional investment organizations.
This is a hands-on engineering role focused on combining investment data, Snowflake, machine learning, and generative AI to solve real-world investment and reporting challenges. You will work alongside investment subject-matter experts, data engineers, architects, analysts, product owners, and client stakeholders to take AI use cases from initial concept through production.
Depending on experience, the role may also include technical leadership, solution architecture, client-facing design sessions, and oversight of engineering standards and AI governance.
Potential use cases include portfolio and benchmark analytics, investment-performance commentary, research synthesis and retrieval, investment-operations exception management, reconciliation support, risk analysis, client and board reporting, and natural-language access to trusted investment data.
Role and Responsibilities:
What You'll Do
AI & GenAI Engineering
- Design and build production AI solutions using machine learning, generative AI, RAG, natural-language analytics, document intelligence, and agentic workflows.
- Develop Snowflake-native AI capabilities using tools such as Cortex Analyst, Cortex Search, Cortex AI Functions, Cortex Agents, Snowpark ML, Feature Store, and Model Registry .
- Build RAG solutions over investment research, manager letters, policies, methodology documentation, reporting commentary, and other enterprise content.
- Develop evaluation frameworks and automated testing for grounding, retrieval quality, accuracy, calculation consistency, prompt behavior, latency, cost, and failure handling.
- Integrate AI capabilities into secure APIs, applications, batch processes, and enterprise workflows.
Investment Data Engineering
- Source, profile, transform, reconcile, and integrate structured and unstructured investment data from Snowflake and other enterprise platforms.
- Develop SQL and Python pipelines supporting positions, transactions, pricing, benchmarks, security master data, corporate actions, cash flows, valuations, NAV, exposures, performance, attribution, and research content.
- Build curated datasets, semantic models, features, and retrieval indexes while preserving business definitions, portfolio hierarchies, effective dates, entitlements, and source-to-report lineage.
- Establish data-quality checks, reconciliation processes, exception thresholds, and monitoring to ensure AI solutions operate against complete, timely, and authorized information.
- Ensure AI-generated insights and narratives remain traceable to approved investment data and methodology.
Solution Delivery & Production Engineering
- Partner with portfolio, performance, operations, risk, reporting, data, and technology teams to translate business needs into practical AI solutions.
- Take solutions from prototype through controlled pilot and production deployment.
- Write maintainable, production-quality code supported by testing, Git-based development, CI/CD, logging, versioning, and technical documentation.
- Implement appropriate security, role-based access, masking, audit logging, and human-review controls for sensitive investment and client data.
- Monitor production solutions for data freshness, model and prompt performance, response quality, drift, latency, cost, usage, and operational issues.
- Maintain deterministic investment calculations within governed analytical systems while using generative AI to retrieve, explain, summarize, investigate, and orchestrate around those calculations.
Technical & Client Leadership
For more senior candidates, responsibilities may also include:
- Leading client discovery, architecture, and solution-design sessions.
- Translating investment-domain requirements into scalable AI and data architectures.
- Establishing standards for AI evaluation, model risk, security, governance, production readiness, and operational handoff.
- Leading architecture and code reviews and providing technical guidance to engineers and analysts.
- Developing reusable reference architectures, libraries, evaluation suites, implementation playbooks, and engineering standards.
- Communicating technical decisions, limitations, risks, costs, and tradeoffs to senior business and technology stakeholders.
What We're Looking For
- Strong professional experience across AI/ML engineering, software engineering, data engineering, data science, or analytics engineering .
- Hands-on experience building AI/ML capabilities that have progressed beyond notebooks or demonstrations into production or client-facing environments.
- Strong Snowflake, SQL, and Python experience, including practical experience with Snowflake AI/ML capabilities.
- Experience building solutions using technologies such as LLMs, RAG, conversational analytics, machine learning, intelligent document processing, or agentic AI .
- Experience working within asset management, asset owners/institutional investing, asset servicing, wealth management, or investment technology .
- Working knowledge of investment data such as portfolios, holdings, transactions, benchmarks, performance, attribution, risk, valuations, NAV, or security master data.
- Understanding of AI evaluation, hallucination and grounding risk, secure data access, privacy, human review, and production monitoring.
- Ability to communicate effectively with both technical teams and investment-domain stakeholders.
Additional Experience We Value
- Experience with investment accounting, IBOR/ABOR, order-management, performance, risk, custodian, fund-administrator, or market-data platforms.
- Familiarity with multi-asset portfolios including equities, fixed income, derivatives, alternatives, and private markets.
- Knowledge of investment concepts such as time- and money-weighted returns, benchmarks, attribution, exposures, valuation, tracking error, or composite reporting.
- Experience with technologies such as dbt, Airflow, Spark, Kafka, Snowpark, Streamlit, MLflow, Docker, Kubernetes, or Terraform .
- Experience with AWS, Azure, or Google Cloud.
- Familiarity with financial-services data governance, model-risk controls, GIPS, SEC marketing requirements, audit expectations, or records-retention requirements.
- Relevant certifications such as CFA, CIPM, CAIA, FRM, SnowPro, cloud architecture, data engineering, or machine learning.
What Success Looks Like
You will help move high-value AI use cases from concept to reliable production use while establishing the data, engineering, evaluation, governance, and monitoring practices needed for investment organizations to trust and scale them.
Successful solutions will demonstrate measurable improvements in areas such as reporting cycle time, analyst productivity, data quality, exception resolution, analytical capacity, accuracy, and client service while leaving behind reusable components and clear documentation that can be maintained and extended over time.
Why Cutter:
At Cutter Associates, we believe people do their best work when they’re trusted, supported, and empowered to thrive - wherever they are.
- Established credibility, entrepreneurial spirit – Founded in 1998 and backed by Nomura Research Institute (NRI), we blend the stability of a global research and technology powerhouse with the agility of a small, fast-moving firm.
- Global reach - We partner with leading investment firms across North America, EMEA, and APAC, giving you meaningful exposure and exciting challenges.
- Fully remote, fully connected - We’re a remote-first company with a flexible work model and a deeply collaborative culture.
- Growth-focused - From client exposure and internal knowledge groups to tailored learning paths, we invest in your development from day one.
- Performance-driven, people-first - We offer competitive pay, bonuses, profit sharing, and benefits that support your whole self.
- A culture that values people - We take pride in our thoughtful, collaborative, and inclusive environment. We’re a team of individuals who care deeply - about our work, our clients, and each other.
Whether you’re launching your consulting career or bringing decades of experience, Cutter is a place where you can thrive.
Benefits at a Glance:
Our benefits vary slightly by location but are designed to support your health, financial wellness, and life outside of work. Highlights include:
- Competitive salary with annual performance bonus and profit-sharing opportunity
- 401(k) match (U.S.) or Superannuation contributions (Australia)
- A generous paid time off package that includes vacation days, sick leave, company holidays, and time off for volunteering
- Comprehensive medical, dental, and vision insurance (U.S.), or regionally competitive coverage (U.K. and Australia)
- Long-term disability and life insurance fully paid by the company (U.S.)
- Flexible work model - fully remote with optional access to co-working spaces or offices
- Global collaboration and travel opportunities across North America, EMEA, and APAC.
Compensation: The anticipated compensation range for this role is $190,000-$260,000 USD plus bonus ( NOTE: CAD compensation ranges are established separately and are not a direct currency conversion of the USD range), depending on factors such as experience, qualifications, market conditions, and geographic location. Final compensation will be determined based on individual background and alignment with the role’s responsibilities.
Salary Context
This $190K-$260K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Cutter Associates, 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 11,857 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($225K) sits 6% above the category median. Disclosed range: $190K to $260K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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: $272,275; VP: $235,000.
Cutter Associates AI Hiring
Cutter Associates has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $260K - $260K.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% of all AI roles offer remote work.
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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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