Principal AI Engineer, DriveWealth

• The ideal candidate is excited to own end-to-end AI solutions grounded in data platform reality, not just prototype them
• 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

Desirable

• 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 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
• 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