Agilent inspires and supports discoveries that advance the quality of life by providing life science, diagnostic and applied market laboratories worldwide with instruments, services, consumables, application and both measurement and asset management expertise.
Builds the data products and pipelines a pod runs on; turns raw domain data into model-ready assets. Pods do not wait for the Fabric to be complete; they build the Fabric through execution, and this role is where that happens for the data plane. Every domain data product built in a pod is constructed to certification standards from the start, because the second consumer of the asset is the point, not an afterthought.
The role goes beyond the conventional pipeline engineering. AI consumption changes what "model-ready" means: data must be retrieval-ready, semantically annotated, contract-governed, and quality-scored, and the AI Data Engineer often uses agents to do the building, generating metadata, resolving entities, and classifying unstructured domain content rather than hand-curating at a scale that cannot hold.
Responsible for
Domain data products and pipelines serving the pod's use case, built to the data plane's certification standards: semantic definition, data contract, entitlement metadata including agent identity, lineage, and certification tier from day one.
Data quality and model-readiness, including quality scoring against the defined dimensions and the quality signals that feed the evals spine; when quality falls below threshold, this role is the one who says so before the agent does something embarrassing with it.
Working relationships with the domain's data owners and stewards, so that steward-validated definitions and certified sources ground the pod's retrieval rather than whatever was easiest to reach.
Retrieval foundations for the pod: structured and unstructured grounding, vector and graph assets where the use case requires relationship reasoning, built on the platform estate rather than parallel infrastructure.
AI-built curation in practice: deploying metadata generation, entity resolution, and content classification agents against the domain's data, contributing those outputs to the registry.
Reusable assets back to the Fabric: every domain data product is a candidate for certification and enterprise reuse, documented and handed to the data plane for promotion, not a point integration that dies with the pod.
Works with the business teams and IT to develop domain data models.
Applying broad understanding of on premise and cloud deployment topologies, creates data collection frameworks to capture, manage, store and utilize large sets of structured, unstructured and/or disconnected data from a wide variety of internal and external sources.
Oversees the establishment of data set processes and builds data structures based on business and technical requirements to funnel data from multiple sources and store using any combination of storage forms (e.g. cloud, local databases) as required.
Consults in design standards and assurance processes for software, systems and applications development to ensure compatibility and operability of data connections, flows and storage requirements.
Creates data tools for analytics and data scientist team members that assist them in building and optimizing our analytics products.
Prepares and manipulates data for predictive and prescriptive modeling. Uses data to discover tasks that can be automated. Identify ways to improve data reliability, efficiency and quality.
Reviews internal and external business and product requirements for data operations and activity and suggests changes and upgrades to systems and storage to accommodate ongoing needs.
What success looks like in year one
The pod's use case running entirely on contract-governed, quality-scored data products, with no undocumented side channels into source systems.
Multiple domain data products from the pod certified into the registry and consumed or queued for consumption by multiple use cases.
Quality signals from the pod's domain flowing into the evals spine, with at least one instance where a quality threshold correctly gated an agent behavior.
Measurably faster data-to-build time driven by reuse and process optimization
Strong data engineering with experience building for AI consumption: retrieval-ready, semantically annotated, contract-governed data products, not only warehouse tables and dashboards.
Hands-on familiarity with the platform estate (Microsoft Fabric, Snowflake, vector and graph stores) and with operating under data contracts, lineage, and certification requirements.
Experience with RAG data foundations: chunking, embedding, hybrid retrieval, and the failure modes that show up in agent behavior rather than in pipeline monitoring.
The disposition to work inside a business domain: you interview stewards, read the pipeline code that produces the data to understand what it actually means, and treat domain knowledge as the scarce input.
Curiosity about AI, its potential and its pitfalls. The field moves monthly, and the people who thrive here are genuinely curious about both sides of it: what these systems can newly do, and where they fail, mislead, or quietly degrade. We want people who read the failure analyses as eagerly as the launch posts, who experiment on their own initiative, and who hold excitement and skepticism at the same time without letting either one win permanently.
Lifelong learners. Whatever expertise a candidate arrives with will be partially obsolete within a year, and that is not a defect of the candidate; it is the condition of the field. We hire people who have reinvented their toolkit before and expect to do it again, who learn in public, and who treat being wrong as information rather than injury. A history of deliberate self-reinvention counts for more than any single credential.
Excellent communication and the ability to influence. Nothing in this organization ships by authority alone. Every AI CoE role here persuades: domain experts to engage, stewards to share what they know, sponsors to stay honest about value, and functions like Legal, Quality, and Security to move from gatekeeping to partnership. We look for people who write and speak clearly, who adapt their register from bench scientist to Board, and who change minds through credibility and clarity rather than escalation.
The instinct to generalize: you build for the second consumer of the asset, not only the first.
Bachelor's or Master's Degree or equivalent.
Typically, at least 8+ years relevant experience for entry to this level.
The full-time equivalent pay range for this position when based in Santa Clara, CA, USA is $172.512,00 - $269.550,00/yr plus eligibility for bonus, stock and benefits. Our pay ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. During the hiring process, a recruiter can share more about the specific pay range for a preferred location. Pay and benefit information by country are available at: https://careers.agilent.com/location
Additional Details
This job has a full time weekly schedule.
Our pay ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. During the hiring process, a recruiter can share more about the specific pay range for a preferred location. Pay and benefit information by country are available at: https://careers.agilent.com/locations
Agilent Technologies Inc. is an equal opportunity employer. Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability or any other protected categories under all applicable laws.
10% of the Time
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No End Date
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