What are the responsibilities and job description for the AI Software Lead Engineer position at Jobright.ai?
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Job Summary:
Lilly India is a global healthcare leader dedicated to making life better for people around the world. They are seeking an AI Software Lead Engineer to shape a next-generation platform that utilizes agentic AI and multi-omics data to accelerate drug discovery, collaborating with domain experts to design and scale systems that support complex scientific workflows.
Responsibilities:
• Design, implement, and scale key components of an agentic AI platform supporting drug discovery workflows, including target-disease association discovery, compound-protein interaction prediction, and multi-omics biomarker identification.
• Build modular backend services and orchestration layers that can support LLM-powered literature mining, real-time experimental planning, and tool-use chains over scientific data including single-cell RNA-seq, spatial transcriptomics, CRISPR screens, and proteomics datasets.
• Collaborate with AI scientists and computational biologists to integrate LLM frameworks (e.g., LangTorch, Semantic Kernel) with structured biological data sources to enable reasoning across multi-omics, assay data, protein-protein interaction networks, metabolic pathways, and experimental results from high-throughput screens.
• Develop intelligent interfaces using React or similar frameworks to support interactive, AI-guided workflows for target identification, pathway enrichment analysis, drug-target network exploration, CRISPR hit validation, and automated experimental protocol generation.
• Ensure data integrity, security, and traceability across workflows that handle omics, compound, assay data, and sensitive biological datasets including patient-derived samples, with proper provenance tracking for regulatory compliance.
• Lead development of scalable services deployed via Kubernetes and Terraform in cloud environments (AWS, GCP) optimized for high-throughput computational biology workloads including genome-wide association studies and molecular dynamics simulations.
• Apply CI/CD, test automation, and observability to enable robust, maintainable deployment pipelines for scientific computing environments supporting real-time experimental feedback and automated hypothesis testing.
• Collaborate with product managers, computational biologists, and domain scientists to translate evolving scientific workflows into scalable software systems that support cutting-edge omics research and accelerate bench-to-bedside translation.
Qualifications:
Required:
• Proven experience as a senior software engineer or tech lead, with a strong foundation in backend architecture, microservices, and distributed systems—ideally in scientific computing platforms supporting multi-omics data or high-throughput biological assays.
• Expertise in Python and proficiency in one or more of the following: Node.js, Go, Java, or Rust. Experience with scientific computing libraries (e.g., NumPy, SciPy, Pandas, BioPython) and omics analysis frameworks is highly desirable.
• Hands-on experience building or scaling platforms in cloud-native environments (AWS, GCP, or Azure) using container orchestration (Kubernetes) and infrastructure-as-code tools like Terraform—preferably for computationally intensive biological workflows such as genome assembly or protein structure prediction.
• Familiarity with frontend development using React or similar frameworks, ideally applied to scientific data visualization or interactive analytics for complex biological datasets.
• Strong interest in applying software engineering to scientific discovery, which includes areas such as: LLM-powered scientific reasoning for hypothesis generation, literature mining, and protocol optimization; AI-driven target identification and CRISPR screening analysis; real-time experimental design optimization using active learning; agentic AI systems for orchestrating multi-step, tool-enabled scientific workflows; high-throughput pipeline development for GWAS, single-cell, or multi-modal omics studies; scientific visualization for networks, pathways, and drug mechanisms; feedback loops that connect wet-lab automation with real-time AI-guided experimentation; multi-omics data integration (e.g., scRNA-seq ATAC-seq, spatial transcriptomics, proteomics/metabolomics co-analysis).
• Strong communication skills and a collaborative mindset for partnering with cross-functional teams including computational biologists, structural biologists, chemical biologists, and lab scientists.
• Intellectual curiosity and a growth mindset—especially an eagerness to deepen your understanding of systems biology, experimental design, and AI applications in drug discovery.
• Bachelor's or Master's degree in Computer Science, Software Engineering, Bioinformatics, Computational Biology, Systems Biology, or a related technical field with coursework in molecular biology, genetics, or biochemistry.
• 7 years of experience in software engineering with a track record of delivering robust, scalable platforms, with demonstrated experience in scientific computing environments supporting biological data analysis or experimental workflows.
• Demonstrated ability to lead engineering projects from architecture to production, ideally in scientific or research environments involving complex biological datasets and multi-step experimental protocols.
Company:
Lilly is a medicine company turning science into healing to make life better for people around the world. Founded in 1993, the company is headquartered in Gurgaon, IN, with a team of 201-500 employees. The company is currently Growth Stage.
