India \- Hyderabad JOB ID: R\-256144 LOCATION: India \- Hyderabad WORK LOCATION TYPE: On Site DATE POSTED: Sep. 29, 2026 CATEGORY: Clinical **Bioinformatics \& AI Engineer** **Location:** Amgen India office, Hyderabad **Employment type:** Full\-time **Department / Team:** Computational Biology, Precision Medicine **Role summary** We are seeking a B**ioinformatics \& 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.