[Remote] Agentic AI Data Engineer (Scientific Data)
Note: The job is a remote job and is open to candidates in USA. BayOne Solutions is seeking an Agentic AI Data Engineer to build and manage data ingestion pipelines for scientific data. The role involves collaborating with various teams to establish data standards and ensuring the integrity and usability of datasets.
Responsibilities
- Build an agentic data ingestion pipeline
- Triage and prioritize incoming requests to ingest specific datasets
- Clean and organize the data. Build the first pass cleaning and organization steps into the agentic flow
- Validate cross-modal linkage. Add automated checks that catch when ingested data does not connect correctly and flag low quality or mismatched records
- Version every dataset. Retain and make prior versions addressable
- Preserve raw data and provenance. Make agent workflows log validation and transformation steps so lineage is traceable
- Make agents usable across teams. Move beyond bespoke steps towards agents that teams can reliably use as a shared, deployed service
- Collaborate with AI, software engineering, and computational biology groups to co-define data standards and conventions
Skills
- Agentic AI engineering: Demonstrated experience building multi-agent workflows or LLM workflows using tools/frameworks such as LangGraph or LlamaIndex, including tool/function calling and asynchronous task execution
- Python data engineering: Strong Python for data manipulation, working with APIs and databases, and handling heterogeneous data formats
- Data versioning and provenance: Familiarity with dataset versioning approaches (e.g. DVC, lakeFS, or equivalent)
- Working knowledge of scientific data structures: Comfortable or willingness to learn common omics data formats like AnnData, H5AD, TileDB
- Basic understanding of omics: No deep bioinformatics expertise required; just a basic understanding of different modalities (e.g. what is RNA-seq vs scRNA-seq vs WES; genomics vs transcriptomics vs proteomics vs metabolomics)
- Unit testing: Comfortable writing unit and functional tests to ensure data processing workflows are reliable and reproducible
- Education: Degree in a technical field or equivalent practical experience
- Experience deploying agent workflows as a shared service (e.g., FastAPI or MCP endpoints)
- Exposure to cloud (AWS, GCP) and containerization (Docker)
- Familiarity with workflow managers such as Nextflow or Snakemake
Company Overview
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