Senior AI Data Engineer, Data Products & RAG Foundations

About this role

Responsibilities

• As a
• Senior
• AI Data Engineer, Data Products & RAG Foundations
• , you will
• be
• a
• data
• engineering SME within a cross-functional AI pod, working alongside AI engineers, domain experts, business stakeholders, data owners, and platform teams.
• Your role is to build
• data
• products, pipelines, metadata, and retrieval-ready assets that power AI-enabled business and scientific workflows across the enterprise.
• Pods do not wait for the
• enterprise
• data foundation
• to be complete; they
• help
• build
• it
• through execution
• .
• Every
• data product
• created by the
• pod is
• designed for governance, reuse
• , and long-term value, with the
• next
• consumer
• in mind from day one.
• Th
• is
• role goes beyond
• traditional da
• ta
• engineering.
• You will work with structured and unstructured data, semantic definitions, quality scoring
• , lineage, contracts, embeddings, vector search, and retrieval foundations for AI systems. You will
• also
• leverag
• e
• AI-assisted
• techniques, such as metadata generation, entity resolution, and conte
• nt classification, to create trusted, AI
• ready
• data products at scale
• .
• You do not need
• prior experience with
• Agilent’s internal data architecture.
• We are looking for a strong data engineer who understands data quality, governance, and AI-ready data foundations and
• is
• excited to help shape the future of enterprise AI at Agilent.
• What
• you will
• do:
• Data Products & Governance
• Build and
• maintain
• AI-ready
• data products and pipelines
• for
• the pod's use case,
• ensuring
• appropriate governance
• ,
• lineage, metadata, access controls, and documentation from the start.
• Design
• data products for reuse, treating every asset as a potential enterprise capability rather than
• a
• point
• integration.
• Da
• ta Quality and Trust
• Estab
• lish
• d
• ata quality
• standards
• ,
• quality scoring
• , and model-readiness criteria
• that support reliable AI behavior and business outcomes.
• Ensure
• q
• u
• a
• lity issues are identified and addressed before the
• y impact downstream
• AI solutions.
• Domain Understanding and Partnership
• Partner
• with
• data owners
• ,
• stewards,
• business stakeholders, and IT teams to
• establish
• trusted
• definitions
• ,
• authoritative sources
• , and domain data models.
• Ensure AI solutions are
• ground
• ed in validated business meaning
• rather than
• co
• nvenience-based access to data
• .
• Retrieval and AI Foundations
• Design r
• etrieval foundations
• that support AI applications
• , including
• structured and unstructured grounding, vector
• search,
• graph
• based approaches, and semantic enrichment
• where
• appropriate
• .
• Apply
• AI-
• assisted techniques such as
• metadata generation, entity resolution, and content classification
• to improve the quality, scalability, and discoverability of
• data assets
• .
• Eng
• ineering Delivery and Reuse
• Design and implement scalable ingestion, integration, and storage frameworks across cloud and
• on-premises
• environments.
• Build reusable data assets, tools, and services that support AI engineers, data scientists, and analytics teams.
• Contribute reusable data products, patterns, and documentation back to the broader enterprise ecosystem.
• ​
• ​
• What success looks like in
• the first year
• The pod's use case
• is
• running
• entirely
• on
• governed, quality-scored data products, with no undocumented
• or unsupported data
• source
• s
• .
• Multiple
• data products
• created
• by
• the pod
• have
• been adopted, reused, or
• identified
• for reuse across
• additional
• AI
• or
• analytic
• s
• use cases.
• Data
• q
• uality signals
• are integrated
• into AI evaluation and
• monitoring
• processes
• ,
• influencing
• AI
• behavior and
• outcomes.
• D
• ata-to-build time
• has measurably
• improved through
• reuse
• , automation,
• and process optimization
• .

Requirements

• Technical Expertise
• Strong data engineering
• building
• AI
• ready dat
• a products
• , not just
• warehouse tables and dashboards.
• Hands-on familiarity with
• platform
• s
• s
• uch as
• Microsoft
• Fabric, Snowflake, vector
• databases,
• graph
• stores, and
• operating
• under data contracts, lineage, and certification requirements.
• Experience with RAG
• foundations
• , including
• chunking, embedding, hybrid retrieval, and
• understanding how
• retrieval
• quality
• impacts
• agent/ AI
• behavior
• and outcome
• s
• .
• Domain
• and
• Product Mindset
• A
• disposition to work
• within
• a business domain
• , partnering with
• data
• stewards
• and su
• bject matter experts
• to understand
• the me
• aning behind the data.
• An
• instinct
• to build
• for
• reuse,
• creating
• assets
• intended for
• second
• consumer
• s
• and use
• cases,
• not just the first
• .
• Communication
• and
• Influence
• Excellent communication and the ability to
• influence
• technical and
• non-technical audiences
• .
• Able to build trusted
• partnersh
• ips
• with domain experts, stewards, business stakeholders and
• functions
• such as
• Legal, Quality, and Security.
• Curiosity
• and
• Growth Mindset
• Curiosity about AI, its
• opportunities
• ,
• limitations
• , staying
• informed
• about
• emerging approaches, while
• maintaining
• a healthy
• skepticism
• and focus on responsible implementation
• .
• A l
• ifelong learner
• who continuously adapts skills
• and
• w
• ays of working in a rapidly evolving field
• .
• Education and
• Seniority
• Bachelor’s or
• Master’s
• d
• egree
• in Computer Science
• ,
• Engineering, Information Systems, Data Science, or a related field,
• or equivalent
• practical experience
• .
• Typically, at least 8+ years
• of
• relevant experience for entry to this level.
• 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.
• Travel Required:
• 10% of the Time
• Shift:
• Day
• Duration:
• No End Date
• Job Function:
• Administration