Senior Engineer 2, Applied Artificial Intelligence

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Welcome to an inspired career.

At Halozyme, we are reinventing the patient experience and building the future of drug delivery. We are passionate about the important work we do and constantly strive to do more. We embrace transformation and work hard to innovate for the future. We do this together, as One Team – we rise by lifting others up and believe in the power of working together for the collective win. That’s why we need you—to help us make a significant impact by taking on increasingly complex challenges, leaping beyond the status quo, advancing our mission and making our One Team culture thrive.

This role is located at our San Diego, CA site.

Join us as a Senior Engineer 2, Applied Artificial Intelligence, and you’ll be part of a culture that welcomes diversity, thinks differently to solve problems, works collaboratively as one team, and delivers meaningful innovations that impact people’s lives.

How you will make an impact

The Senior Engineer 2, Applied AI builds and deploys AI solutions that directly support business workflows across Commercial, Regulatory, Quality, Finance, Operations, and Corporate functions. This role focuses on turning real business problems into working AI applications—including copilots, retrieval-augmented generation (RAG) solutions, document generation, automation agents, predictive models and decision-support tools. The Senior Engineer 2, Applied AI works closely with business SMEs, Data Engineering, and the AI Governance team to ensure solutions are secure, compliant, explainable, and production-ready in a regulated life-sciences environment.

In this role, you’ll have the opportunity to:

• Build AI applications such as enterprise copilots, search assistants, document intelligence and generation tools, workflow-automation agents, predictive models, decision‑support tools, and reusable AI components including prompt libraries and solution patterns
• Implement Retrieval-Augmented Generation (RAG) pipelines leveraging enterprise data sources such as SharePoint, data lakes, document repositories, and research systems
• Build and maintain end‑to‑end AI/ML pipelines including data ingestion, feature engineering, model training, evaluation, deployment, and monitoring
• Integrate LLMs into business workflows using APIs and platforms such as Azure OpenAI, OpenAI, Anthropic, and AWS Bedrock
• Develop prompt-engineering, grounding, and evaluation frameworks to improve accuracy, reliability, and alignment
• Translate business use cases across domains (e.g., medical affairs, regulatory, commercial, finance) into functional AI prototypes and production-ready applications
• Collaborate with Data Scientists to scale models into production systems and with Product Owners/SMEs to refine requirements, acceptance criteria, and success metrics
• Deploy and maintain AI solutions on cloud platforms using modern APIs and software‑engineering best practices
• Implement MLOps and LLMOps capabilities including versioning, monitoring, logging, performance tracking, observability, and workload cost optimization
• Implement guardrails and controls to prevent data leakage, hallucinations, and misuse
• Integrate AI solutions with enterprise identity and data‑security frameworks, including RBAC, Purview, and related governance tools
• Ensure all AI systems are reliable, scalable, and secure, and that they comply with data‑classification rules, privacy requirements, and AI governance policies

Key Skills

• Applied AI/ML engineering
• Prompt engineering & grounding techniques
• Generative AI & LLM integration (Azure OpenAI, OpenAI, Anthropic, AWS Bedrock)
• Enterprise data integration (SharePoint, data lakes, document repositories)
• RAG architectures, vector databases, and semantic search
• Cloud and API application development (Azure/AWS/GCP)
• Python engineering
• MLOps / LLMOps (monitoring, logging, versioning, observability, cost optimization)
• Security‑aware engineering (RBAC, Purview, guardrails)
• Responsible AI, governance, explainability, and data‑classification frameworks
• Business problem‑solving & systems thinking
• Strong stakeholder communication and cross‑functional collaboration

Preferred Experience

• Experience with R