Job Description: Lead Bioinformatics Engineer (Multi-Omics & AI Platform) C3/C4
We are hiring a Lead Bioinformatics Engineer to drive architecture and engineering for an enterprise-scale Multi-Omics & AI platform. This is a systems and platform-building role, focused a production-grade data harmonization engine that transforms fragmented omics data into ML-ready feature stores.
1. Multi-Omics Data Platform Architecture
- Building automated harmonization engine for multi-modal data (genomics, transcriptomics, proteomics, metabolomics)
- Develop scalable ETL pipelines to ingest and standardize formats (FASTQ, BAM, VCF, H5AD, MTX, GCT)
- Architect ML-ready feature stores using optimized formats (Zarr, Apache Arrow, TileDB)
- Define 3-tier data models (Dataset, Sample, Feature) linking clinical and molecular data
- Implement ontology mapping using standards (Ensembl, HUGO, NCBI)
- Embed batch correction & normalization (e.g., ComBat, Harmony)
2. Pipeline Engineering & MLOps
- Build production-grade pipelines for RNA-seq, WGS, and proteomics
- Scale data processing on cloud (AWS preferred: S3, EC2, Batch, Athena)
- Implement reproducible workflows using Nextflow / Snakemake + Docker/Singularity
- Design high-performance APIs (Python/R) for data access and model consumption
- Leverage data lake/lakehouse architectures (Iceberg, Delta Lake)
3. AI & Data Governance Leadership
- Translate biological problems into ML-ready datasets and features
- Define data validation, contracts, and quality checks
- Implement CI/CD and data quality frameworks (Great Expectations, Pydantic)
- Ensure data integrity and prevention of bias/data leakage in AI models
Required Qualifications
Experience- Masters/PhD in Bioinformatics, Computational Biology, Computer Science, or related field
- 5+ years experience in bioinformatics data engineering or platform development
- Proven experience building production-grade data platforms / data lakes for ML
- Hands-on experience with multi-omics integration (=3 modalities)
- Exposure to AI/ML pipelines (deep learning, embeddings, LLMs in biology)
Technical Skills- Strong expertise in NGS data processing (WGS, WES, RNA-seq, single-cell etc) using tools like GATK, BWA, SAMtools, STAR, Kallisto and frameworks such as Scanpy, Seurat, Bioconductor, AnnData etc
- Deep understanding of variant calling, gene expression analysis, pathway analysis, and single-cell data workflows etc
- Proven experience in bioinformatics ETL, data harmonization, and processing large-scale multi-format omics datasets
- Hands-on with optimized data structures like AnnData/H5AD, Zarr, Apache Arrow, TileDB, including sparse matrix handling
- Experience building scalable scientific data platforms/data lakes on AWS (S3, EC2, Batch, Athena) with Delta Lake / Iceberg
- Strong in workflow orchestration (Nextflow/Snakemake), containerization (Docker/Kubernetes/Singularity), and CI/CD automation
- Proficient in Python (Pandas, NumPy, Scikit-learn) with exposure to PyTorch/TensorFlow for ML integration
- Experience in creating ML-ready datasets, feature engineering pipelines, and ensuring data validation (bias, leakage, quality checks)
