AI Data Engineer

Terumo BCT, Inc.•Denver, CO

1d•Onsite

About The Position

Working under general direction, designs, builds, and owns the data that powers AI‑driven agents and decision systems in production enterprise environments. This role is responsible for ensuring that AI agents have access to the right data, in the right form, at the right time, so that models can reason effectively, respond accurately, and integrate safely into business workflows. The AI Data Engineer focuses on context engineering and data enablement for AI: acquiring, normalizing, structuring, and surfacing data for use by LLM‑ and hybrid AI agents. ETL, transformation, and data integration are important tools in service of this goal, but the core responsibility is owning the data inputs, representations, and feedback loops that shape agent behavior and outcomes. The role emphasizes data semantics, system context, evaluation, and collaboration across engineering, product, and governance to deliver AI systems that are reliable, auditable, and scalable.

Requirements

Bachelor’s degree in Computer Science, Data Science, Engineering, AI/ML, Linguistics, or a related field; or equivalent education and experience sufficient to perform the role.
Minimum 4 years demonstrated experience owning and delivering data that supports AI or decision systems in production.
Experience designing data ingestion, transformation, and normalization processes for downstream consumers.
Experience supporting AI or advanced analytics use cases where data structure and context directly impact system behavior.
Experience analyzing data using Python; SQL familiarity preferred.
Strong reasoning about data semantics, context, and edge cases.
Ability to think beyond pipelines and focus on how data enables system behavior and outcomes.
Clear written and verbal communication skills.
Comfort operating in ambiguous, fast‑evolving technical environments.

Nice To Haves

Experience with AI agents, retrieval‑based systems, or hybrid AI architectures.
Familiarity with cloud‑based data and AI platforms.
Experience designing or using evaluation frameworks for AI‑driven systems.

Responsibilities

Own the data inputs and contextual information used by AI agents, including operational data, reference data, metadata, and derived representations.
Design and implement data ingestion, transformation, and normalization processes that make heterogeneous data usable by AI systems.
Define how data is exposed to agents (e.g., retrieval interfaces, structured context, tool inputs) so models can reason over it effectively.
Ensure data coverage, quality, and consistency across common and edge‑case scenarios.
Shape how data is represented for AI consumption, including structured schemas, constraints, and contextual packaging.
Work closely with agent and platform engineers to define data contracts between AI systems and downstream services.
Balance completeness, latency, cost, and interpretability when designing data access patterns for agents.
Design and maintain evaluation datasets and measurement frameworks to assess AI behavior, reasoning quality, correctness, and safety.
Analyze production signals and usage data using Python to identify gaps in data, context, or representations.
Use evaluation results and real‑world feedback to iteratively improve the data supplied to AI agents.
Partner with software engineers to integrate data pipelines and context services into AI‑enabled workflows.
Work with product, compliance, and risk stakeholders to ensure data used by AI systems meets business, regulatory, and governance requirements.
Document data sources, transformations, assumptions, and known limitations to support transparency and long‑term maintainability.
Contribute to standards and best practices for data‑centric AI system design.
Evaluate emerging tools, platforms, and approaches related to AI data enablement and evaluation.
Support knowledge sharing through documentation, reviews, and cross‑team collaboration.