Principal AI / Machine Learning Data Engineer - Remote or hybrid from MN or DC

UnitedHealth Group•Eden Prairie, MN

3d•$112,700 - $193,200•Hybrid

About The Position

The Principal AI Data Engineer will design and build end-to-end AI pipelines for large-scale unstructured data, enabling advanced analytics, Generative AI, and investigative insights. This role will transform raw, complex datasets—such as scanned documents, images, PRFs and other OCR- driven unstructured data sources—into AI-ready, searchable, and model-integrated data products. You will play a key role in building LLM-powered systems (e.g., RAG, semantic search, summarization, and insight extraction) and scaling them into production environments. This position sits at the intersection of data engineering and AI, with an emphasis on building modern data pipelines and enabling production-grade AI capabilities. You’ll enjoy the flexibility to work remotely from anywhere within the U.S. as you take on some tough challenges. For all hires in the Minneapolis or Washington, D.C. area, you will be required to work in the office a minimum of four days per week.

Requirements

Bachelor’s degree or equivalent experience
5+ years of experience designing, building, and operating scalable data pipelines and platforms (batch + streaming)
2+ years of experience deploying Generative AI solutions to production (e.g., RAG, LLM-powered pipelines, semantic search)
Proven solid hands-on development in Python and SQL, with experience in Spark/PySpark and Databricks (or similar distributed platforms)
Experience building ingestion and processing frameworks for unstructured data (OCR, documents, images), including parsing and enrichment
Experience with cloud platforms (AWS/Azure/GCP), DevOps/CI/CD, and infrastructure-as-code, including secure handling of sensitive data (PII/PHI)
Proven ability to design scalable solutions, implement data quality/observability practices, and collaborate across stakeholders
Solid hands-on engineering in Python and SQL; familiarity with JVM languages (Java/Scala) in Spark ecosystems
Familiarity with security and privacy concepts for data platforms (e.g., least privilege, PII/PHI handling) and working with compliance partners

Nice To Haves

Experience with cloud platforms such as AWS, Azure, or Google Cloud, including managed data services
Experience with streaming and event-driven architectures (e.g., Kafka, Kinesis, Event Hubs)
Experience with data quality and validation frameworks (e.g., Great Expectations, Deequ) and/or data observability tooling
Experience enabling MLOps practices (e.g., feature stores, model registries, experiment tracking, deployment automation)
Experience with lakehouse architectures, Delta Lake, and advanced Spark optimization/performance tuning
Experience with data visualization tools and libraries such as Plotly, seaborn, and Chartjs
Experience with machine learning and predictive analytics

Responsibilities

Design, develop, and maintain scalable data pipelines and data platforms supporting analytics, machine learning, and AI use cases
Build and optimize ingestion frameworks for large-scale structured and unstructured data, including streaming and event-driven sources
Partner with cross-functional stakeholders to understand evolving data and AI needs and define long-term technical solutions
Enable and support machine learning and AI workflows, including feature engineering, data preparation, and model deployment support
Drive strategic initiatives around Generative AI, data quality, observability, lineage, and governance
Develop and maintain frameworks that support rapid experimentation and deployment of AI/ML solutions
Introduce and evolve best practices in data modeling, orchestration, testing, and monitoring
Identify and champion opportunities for platform scalability, performance optimization, and cost efficiency
Collaborate with product, analytics, and infrastructure teams to deliver high-impact data and AI solutions
Build and maintain reusable parsing, enrichment, analytic, and service libraries to accelerate delivery across teams
Work comfortably under time-sensitive conditions while ensuring thoroughness
Maintain high ethical standards and the ability to remain objective and confidential
Build and operate production data platforms and pipelines across batch and streaming workloads
Work hands-on engineering in Python and SQL; in a JVM languages (Java/Scala) Spark ecosystems
Utilize distributed processing and lakehouse/warehouse patterns (eg, Spark/PySpark, Databricks, Snowflake)
Build pipelines for OCR, document parsing, and text extraction from image-based or scanned data sources
Enable Generative AI solutions in production (eg, RAG-style architectures), including retrieval patterns and evaluation/monitoring practices
Take a knowledge-centric data approaches (eg, metadata-driven systems, entity resolution, and/or graph concepts) to improve discoverability and downstream analytics
Implement data quality, observability, and monitoring (profiling, validation, alerting, and reliability improvements)
Utilize orchestration, CI/CD, containerization, and infrastructure-as-code (eg, Airflow, GitHub Actions, Docker, Terraform, Kubernetes)
Work in the Cloud (AWS, Azure, and/or GCP), including secure handling of sensitive data (PII/PHI) and collaboration with compliance partners
Lead through influence, mentor engineers, and translate ambiguous problems into scalable technical roadmaps