Bioinformatics & AI Engineer

Career CategoryClinicalJob Description
Bioinformatics & AI Engineer
Location: Amgen India office, Hyderabad
Employment type: Full-time
Department / Team: Computational Biology, Precision Medicine
Role summary
We are seeking a Bioinformatics & AI Engineer to build, evaluate, and deploy deep learning and foundation-model-enabled systems that accelerate biomarker discovery, translational research, and clinical development. This individual contributor role combines bioinformatics, machine learning, and software engineering to turn genomic, multi-omics, imaging, and clinical data into reliable, traceable scientific capabilities. The engineer will develop and evaluate biological foundation-model applications and supporting platforms, working closely with computational biologists, data engineers, translational scientists, and clinical teams.
Key responsibilities
Design, develop, validate, and operate foundation-model-enabled applications for genomics, transcriptomics, single-cell and spatial omics, proteomics, imaging, and clinical data.
Adapt and evaluate biological foundation models, protein and sequence models, multimodal models, and large language models for biomarker discovery, target identification, patient stratification, and scientific decision support.
Build robust model development workflows spanning data curation, representation learning, fine-tuning or parameter-efficient adaptation, retrieval augmentation, evaluation, and monitored deployment.
Engineer scalable, reproducible pipelines for preparing and harmonizing multi-omics and clinical datasets, with clear provenance, versioning, quality controls, and fit-for-purpose access controls.
Develop agentic workflows that combine foundation models with validated bioinformatics tools, structured knowledge, and human review to support research planning, quality control, analysis execution, and result interpretation.
Define rigorous benchmarking and validation strategies, including biological relevance, robustness, bias assessment, uncertainty, hallucination risk, and reproducibility for models and AI-enabled workflows.
Partner on real world data projects and establish utility for precision medicine applications
Partner with computational biology, wet-lab, clinical, data engineering, and product teams to translate scientific needs into usable, well-documented technical solutions.
Develop production-ready services and interfaces using cloud and GPU infrastructure; optimize performance, cost, reliability, and observability for large-scale data and model workloads.
Produce clear technical documentation, model cards, evaluation reports, and methods descriptions suitable for internal review, regulated development contexts, and scientific publication.
Troubleshoot end-to-end platform and pipeline issues, promote engineering best practices, and contribute to a culture of scientific rigor and responsible AI use.
Required qualifications
Education & experience
Master’s or PhD in Bioinformatics, Computational Biology, Computer Science, Machine Learning, Statistics, Genetics/Genomics, or a related discipline.
7+ years of hands-on experience building bioinformatics, machine learning, data science, or research software solutions; experience applying AI to biomedical or life-science data is strongly preferred.
Technical skills
Strong programming skills in Python and practical experience with software engineering practices, including Git, testing, code review, CI/CD, and documentation.
Hands-on expertise with deep learning and foundation models, including transformers, self-supervised learning, embedding models, fine-tuning or parameter-efficient adaptation, evaluation, and inference optimization.
Experience using or adapting biological foundation models for sequence, protein, cellular, molecular, or multimodal biomedical data; familiarity with LLMs, retrieval-augmented generation, and tool-using agents.
Experience with Hugging Face and AWS Sagemaker.
Strong understanding of genomics, transcriptomics, single-cell or spatial omics, proteomics, imaging, or other biomedical data modalities and their analytical limitations.
Experience designing reproducible data and analysis workflows using workflow engines such as Nextflow or Snakemake and containers such as Docker or Singularity.
Experience with cloud and HPC environments, GPU compute, distributed training or inference, and scalable data processing frameworks.
Working knowledge of biological data formats and standards, including FASTQ, BAM/CRAM, VCF/MAF, HDF5, AnnData, Seurat, and metadata best practices.
Experience curating, integrating, and governing data from public biological and clinical resources such as TCGA, GTEx, GEO, SRA, dbGaP, cBioPortal, ClinVar, CellxGene, COSMIC, gnomAD, and UniProt.
Ability to design scientifically meaningful benchmarks and communicate model performance, limitations, uncertainty, and responsible-use guidance to technical and scientific stakeholders.
Strong statistical reasoning and experience applying quality control and appropriate evaluation methods to biological data and machine learning systems.
Experience in a biomedical, pharmaceutical, or regulated research environment is preferred.
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