Principal Data Engineer, GenAI

Benefits Overview

A career at CoBank can offer you the opportunity to make a personal impact on the people and communities where we do business. In order to be the best, we hire the best!

Remarkable Benefits Offered by CoBank

• Careers with a purpose
• Time-Off Packages, 20 days of vacation, 10 paid sick days and 11 paid holidays
• Competitive Compensation & Incentive
• Hybrid work model: flexible arrangements for most positions
• Benefits Packages, including Medical, Dental and Vision coverage, Disability, AD&D, and Life Insurance
• Robust associate training and development with CoBank University
• Tuition reimbursement for higher education up to $10k per year
• Outstanding 401k: up to 6% matching and additional 3% non-elective contribution & Student Loan Match
• Community Impact: United Way Angel Day, Volunteer Day and Associate Directed Contribution
• Associate Resource Groups: creating a culture of respect and inclusion
• Recognize a fellow associate through our GEM awards

Job Description

We are seeking a Principal Data Engineer, GenAI to lead the integration of generative AI capabilities into our data platform. In this role, you will blend deep expertise in data engineering with cutting-edge AI/ML integration, building scalable pipelines and infrastructure that power next-generation AI applications. You will design and optimize data architecture and pipelines (both batch and real-time) to support Retrieval-Augmented Generation (RAG), and adapter-based fine-tuning of LLMs using proprietary domain data, leveraging cloud-native technologies (primarily AWS) and open-source tools. This role requires close collaboration with cross-functional teams (Data Engineering, Data Science, Api, Experience, Product, Risk, Compliance etc) to deliver innovative, AI-driven solutions aligned with business goals, while ensuring our data systems are secure, compliant, and highly performant.

Essential Functions

• Leads the design and optimization of scalable data pipelines and architecture to support GenAI use cases, including Retrieval-Augmented Generation (RAG) and adapter-based fine-tuning of LLMs using proprietary domain data.
• Builds and manages embedding workflows leveraging frontier LLMs (e.g., OpenAI, Anthropic, Meta, Mistral) and ensures efficient storage and semantic retrieval of vectorized content using vector databases such as FAISS, Weaviate, OpenSearch, Azure AI Search, or AWS Kendra.
• Develops ingestion and transformation workflows for unstructured content (e.g., PDFs, reports, emails), including chunking, semantic tagging, and metadata enrichment to power GenAI and semantic search applications.
• Partners with engineering, product, and data stakeholders to prototype and scale GenAI services, while establishing patterns and frameworks that lay the foundation for future ML and AI capabilities at CoBank.
• Defines and enforces data governance, quality, and compliance standards across GenAI pipelines for both structured and unstructured data, ensuring alignment with regulatory requirements (e.g., CCPA, GDPR, EU AI Act) and industry frameworks such as ISO/IEC 400, NIST AI Risk Management Framework (AI RMF), and OECD AI Principles.
• Provides architectural leadership and technical mentorship, ensuring integration of GenAI systems into CoBank’s analytics and EKS-based microservices platform, and aligning with existing CI/CD, GitOps and observability frameworks.

Education

• Bachelor's Degree in Computer Science, Information Technology, or related field. required
• Bachelor's degree may be substituted with four years of related experience (four years is in addition to what is minimally required for the role), or an equivalent combination of education and related experience.
• Master's Degree in Computer Science, Information Technology, or related field. preferred

Work Experience

• 10 years of progressive experience in data engineering, with a strong foundation in building scalable, secure, and high-performance data pipelines, including both batch and real-time architectures. required
• 5 years of experience leading data architecture and engineering efforts in cloud environments (preferably AWS), with demonstrated expertise in tools such as Apache Spark, SQL, Python, Airflow, Kafka, and modern data lake/lakehouse architectures. required
• 3 years of hands-on experience with GenAI/ML infrastructure, including experience designing and building embedding pipelines, vector databases (e.g., FAISS, Weaviate, OpenSearch), and Retrieval-Augmented Generation (RAG) architectures. required
• Prior Experience mentoring technical teams and guiding architectural decisions across cross-functional stakeholders in engineering, product,