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