Bioinformatics & AI Engineer

Career Category Clinical Job 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.   .