Sr. Software Engineer, Enterprise AI Enablement Engineer

Moderna; Cambridge (MA); On-site; USD 150,000 - 190,000; Full time

Job summary

Moderna in Cambridge, MA is seeking a Sr. Software Engineer, Enterprise AI Enablement to turn high-value AI prototypes into secure, scalable products.

You will partner with science, business, product, platform, data, and security teams to shepherd applications from architecture to production ownership. As an advanced individual contributor, you will define reusable engineering patterns, lead end-to-end productionization, and design modular services and APIs while ensuring security, traceability,

Qualifications

• Bachelor’s degree in CS or related field or equivalent experience.
• At least 5 years in software/data/compute engineering with 2+ years in ML/LLM/agentic AI.
• Strong Python skills and experience with TypeScript/JavaScript.
• Experience designing modular services and APIs with CI/CD, containers, and cloud practices.
• Hands-on experience with LLM/agent workflows, including model APIs and evaluation.
• Experience turning prototypes into durable, production-grade products.

Responsibilities

• Lead end-to-end productionization of AI applications from discovery to deployment.
• Refactor early-stage apps into modular services, APIs, UIs, and data models.
• Provide technical direction across multiple initiatives with limited oversight.
• Create reusable agent skills, playbooks, and deployment patterns.
• Design and evaluate LLM workflows, tool use, and boundaries between components.
• Embed security, privacy, and compliance throughout the engineering lifecycle.

Skills

Python

TypeScript/JavaScript

APIs

CI/CD

Cloud engineering

LLM/agentic AI

Data modeling

Debugging at scale

Education

Bachelor's degree in CS or related field

Master's degree (preferred)

Tools

AWS

PostgreSQL

Redshift

Athena

Job description

The Role

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 in Computer Science, Bioinformatics, Computational Biology, Data Science, Engineering, or a related field.
• Experience developing software, computational platforms, or AI-enabled workflows within biotechnology, life sciences, research, or another scientifically complex environment.
• Experience with AWS or comparable cloud platforms, high‑throughput data processing, workflow orchestration, and technologies such as PostgreSQL, Redshift, Athena, or their equivalents.
• Experience building reusable agent infrastructure, model‑provider abstractions, evaluation frameworks, contextual retrieval systems, or standardized application‑development harnesses.
• Familiarity with enterprise and open‑weight language models and the performance, security, cost, and operational considerations involved in selecting and deploying them.
• Experience operating in regulated or quality‑sensitive environments, including secure data handling, provenance, auditability, architecture review, and validation practices.