Principal AI Engineer

About Us

DriveWealth is on a mission to make investing easier. We believe that everyone should have the ability to control their financial future, and that access to financial markets should not be limited by geography, wealth, or legacy systems. We are a global B2B financial technology organization dedicated to democratizing access to financial independence around the world. Our mission is realized through an API-based platform, empowering our partners to offer seamless investing and trading experiences to clients worldwide, all from their mobile devices. Our technology provides partners with a modern, extensible toolkit, enabling traditional investment workflows and innovative techniques like fractional share ownership. DriveWealth has evolved into a global platform offering trading of US equities, mutual funds, ETFs, fixed income, and options.

There’s never been a better time to build a category-defining business and there has rarely been a team better positioned for this opportunity. Our culture blends the pace and agility of a fintech start-up with the impact, stability, and discipline of Wall Street. We encourage creativity and experimentation while ensuring institutional-grade execution and regulatory compliance in everything we do. Join us and help build the future of global investing!

About the Role

We are looking for a Principal AI Engineer who thrives at the intersection of data platform engineering and applied artificial intelligence. You will bring AI-native thinking to our data ecosystem and lead building intelligent pipelines, embedding models into data workflows and creating AI-powered analytics capabilities that transform how the business consumes and acts on data. The ideal candidate is excited to own end-to-end AI solutions grounded in data platform reality, not just prototype them.

The Data Analytics organization at DriveWealth powers the company's data ecosystem end-to-end. This role sits within the Data Platform Engineering team and introduces a third critical pillar alongside Data Ingestion and the Semantic Data Layer:

Applied AI Intelligence Layer - Focused on designing and deploying AI/ML-powered capabilities that sit on top of our data platform, enabling smarter analytics, automation and insight generation at scale

What You'll Do

Design, build and deploy AI and ML-powered solutions that operate on top of our data platform, including LLM integrations, RAG pipelines, embedding workflows and intelligent agents

Build and maintain the data infrastructure that supports AI use cases: feature stores, vector databases, model input/output pipelines and evaluation datasets

Partner with data engineers, analysts and product teams to identify where AI can automate, augment or accelerate data workflows and analytical decision-making

Develop AI-assisted data quality, anomaly detection and observability capabilities that improve the reliability and trustworthiness of our data products

Establish best practices for responsible AI development on data systems, including prompt engineering standards, model evaluation frameworks, versioning and documentation

Contribute to self-service AI-powered data products that make data more accessible to all consumers like natural language interfaces, intelligent semantic search, automated insight surfacing

Mentor and support other engineers in building AI literacy and integrating AI-first approaches into data platform work

You Bring

5+ years of professional experience in data engineering, ML engineering or a related field, with a demonstrated track record of taking AI/ML solutions from concept to production.

Technical Skills:

Strong proficiency in Python and SQL

Hands-on experience with LLMs, prompt engineering, RAG architectures and AI orchestration frameworks (e.g. LangChain or equivalent)

Familiarity with vector databases and embedding pipelines

Proficiency with Databricks and AWS, including ML-oriented services (e.g. SageMaker, MLflow, Databricks Model Serving)

Experience with data transformation and orchestration tools (e.g. dbt, Airflow)

Familiarity with infrastructure as code and MLOps practices (e.g. Terraform, CI/CD for model deployment)

Solid understanding of data platform fundamentals - pipeline design, data modeling, semantic layers; and how AI capabilities integrate with and depend on them

Proven ability to work cross-functionally with product, engineering, analytics and business stakeholders to translate AI opportunities into shipped solutions

Curiosity, accountability and a drive to apply AI thoughtfully with attention to data quality, model reliability and real-world usability

BS in Computer Science, Data Science or equivalent

Special Knowledge (Nice to Have, But Not Required)

Fintech or capital markets experience and awareness of compliance constraints relevant to AI (e.g. model explainability, auditability)

Experience with agentic AI systems and multi-step reasoning workflows

Familiarity with AI evaluation framework…