LLM Data Engineer

Role: LLM Data Engineer

Location: REMOTE

Must have:

AWS, Data Sets, data sources, data services with in AWS

Strong AI & LLM

Recent healthcare industry exp (HIPAA, hl7, etc)

LinkedIn Page

Strong communication

Context

: We are looking for a Generalist Data Engineer for one of our clients building a healthcare-focused AI benchmark and evaluation suite. The initial target is for clinical prediction tasks including sepsis onset, days-to-death, and lab value trend forecasting, evaluated across multiple frontier and vertical-specific models.

Role Summary You will be responsible for everything that happens to data before a model sees it and after a model responds. You will build the ingestion, normalization, and packaging layer that turns the raw healthcare data into standardized benchmark inputs, and the storage and query layer that makes evaluation results analyzable.

What You will Own

* Build ingestion and normalization pipelines for three distinct modality families: DICOM radiology studies, whole-slide pathology images, and tabular EHR extracts (labs, vitals, encounters, medication administration records).

* Design the canonical benchmark record format, which is the intermediate representation that every task configuration and every model adapter reads from, so that "full EHR record" and "image only" variants of the same task are provably drawing from the same underlying case.

* Solve the modality packaging problem: gigapixel pathology slides and multiseries radiology studies must be reduced to payloads that fit inside third-party API limits (48 images, 20 MB) without silently destroying diagnostic signal. You will build the tiling, region selection, downsampling, and compression strategies, and the provenance metadata that records exactly what was sent so results remain reproducible and defensible.

* Construct the cohort and label pipelines behind the prediction tasks: timewindowed feature assembly for sepsis onset, survival horizon calculation for days-to-death, and longitudinal series construction for lab value trends.

* Build the results store and analysis layer per-run, per-model, per-task scored outputs.

* Enforce PHI handling discipline end to end: encryption at rest and in transit, least-privilege access, audit logging, deidentification where required, and clear boundaries around what leaves the VPC when a payload goes to a third-party API.

Required Skills

* Data engineering: Production-grade pipeline code, not notebooks. Strong testing discipline with developing deterministic, idempotent, re-runnable jobs.

* AWS data stack: Deep hands-on experience with S3 (layout design, lifecycle policies, storage class economics at image scale), AWS Glue and/or Spark on EMR, Athena, Step Functions, Lambda, and AWS Batch.

* SQL and data modeling: Complex temporal joins, point-in-time correctness, and the discipline to avoid label leakage in time-series clinical data.

* Data quality and lineage: Validation frameworks, schema enforcement, and versioned datasets.

* AWS security and governance: IAM policy design, KMS, VPC endpoints and PrivateLink, and working inside a HIPAA-eligible architecture with a BAA in place. Desirable Skills

* Healthcare data formats and standards: DICOM, FHIR, HL7v2, OMOP CDM, and the practical realities of clinical coding (ICD, LOINC, RxNorm, SNOMED).

* Medical imaging handling: pydicom, OpenSlide, and the basics of WSI pyramid structure and tiling.

* Infrastructure as code (Terraform) development.

* Containerization and CI/CD: Docker, ECR, and a mainstream CI system.

* Enough familiarity with LLM APIs and multimodal payload construction. Nice to Have

* Prior work on clinical prediction models or healthcare ML datasets (MIMIC, eICU, or equivalent institutional data).

* Experience with AWS HealthImaging or HealthLake.

* Familiarity with de-identification tooling and re-identification risk assessment.