Sr. AI Engineer - Asset Management, Investment Data & Reporting

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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….