Sr. Software Engineer, Enterprise AI Enablement Engineer

The Sr. Software Engineer, Enterprise AI Enablement Engineer will turn high-value AI prototypes into secure, reliable, scalable products. As an advanced individual contributor within Enterprise AI Enablement, you will partner with scientific, business, product, platform, data, and security teams to take early-stage applications through architecture, migration, evaluation, deployment, and transition to durable ownership. You will provide technical leadership across the application, data, cloud, and AI layers while establishing reusable engineering patterns that help Moderna move from experimentation to production more quickly—without sacrificing quality, security, traceability, or cost discipline. Here’s What You’ll Do Lead the end-to-end productionization of agentic AI applications, from technical discovery and architecture through development, testing, deployment, stabilization, and operational handoff. Assess and refactor early-stage applications into modular, maintainable services, APIs, user interfaces, and data models while preserving business intent and feature parity. Provide technical direction across multiple concurrent initiatives, managing dependencies, risks, platform constraints, and delivery tradeoffs with limited oversight. Create reusable agent skills, migration playbooks, reference architectures, evaluation harnesses, and deployment patterns that accelerate delivery across related teams. Design and evaluate LLM and agent workflows, including tool use, structured outputs, contextual retrieval, model selection, failure testing, and appropriate boundaries between deterministic software and model-based reasoning. Diagnose complex issues across application, data, platform, and infrastructure layers using logs, metrics, test harnesses, and systematic root-cause analysis; automate solutions to recurring problems. Embed security, privacy, data classification, traceability, and compliance requirements throughout the engineering lifecycle, partnering with the appropriate review teams when needed. Produce high-quality architecture documents, technical specifications, API contracts, test plans, runbooks, and handoff materials that support reproducibility and long-term ownership. Define and track meaningful measures of engineering impact, including time to production, reliability, throughput, reuse, adoption, operational cost, and reduction of manual effort. Here’s What You’ll Need (Minimum Qualifications) Bachelor’s degree in Computer Science, Software Engineering, Bioinformatics, Computational Science, Data Science, or a related discipline, or equivalent practical experience. At least 5 years of professional software, data, or computational engineering experience, including at least 2 years working with machine learning, LLM, or agentic AI applications. Strong programming skills in Python and experience with TypeScript, JavaScript, or another modern application-development language. Demonstrated experience designing modular services and APIs, working with relational databases and data models, and applying automated testing, version control, CI/CD, containerization, and cloud engineering practices. Hands-on experience with LLM or agent workflows, including model APIs, tool or function calling, prompt and context design, structured outputs, evaluation, and failure-mode testing. Experience converting prototypes into maintainable products, including dependency management, environment capture, observability, documentation, deployment, and operational handoff. Proven ability to troubleshoot complex application and data-platform problems, identify root causes, evaluate architectural tradeoffs, and deliver durable improvements. Ability to lead technical work across cross-functional teams, communicate effectively with technical and nontechnical stakeholders, and manage competing priorities with limited supervision. Here’s What You’ll Bring to the Table (Preferred Qualifications) Master’s degree or PhD