Principal AI Engineer

DriveWealth•Austin, TX

7h•$265,000 - $285,000•Hybrid

About The Position

DriveWealth is seeking a Principal AI Engineer to join their Data & Analytics organization, specifically within the Data Platform Engineering team. This role focuses on integrating AI and ML into the company's data ecosystem, leading the development of intelligent pipelines, embedding models into data workflows, and creating AI-powered analytics capabilities. The position requires an individual who can own end-to-end AI solutions, grounded in data platform realities, rather than just prototyping them. The Applied AI & Intelligence Layer is a new pillar alongside Data Ingestion and the Semantic Data Layer, aimed at designing and deploying AI/ML-powered capabilities for smarter analytics, automation, and insight generation at scale.

Requirements

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.
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

Nice To Haves

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 frameworks and dataset curation for fine-tuning or RLHF
Prior experience mentoring engineers or contributing to AI/ML best practice development within a team

Responsibilities

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