Senior Engineer 2, Applied Artificial Intelligence

Halozyme, Inc. • San Diego, CA

1d

About The Position

Save time and apply through your LinkedIn account. Click the Apply with LinkedIn button and your LinkedIn profile will be imported into our site. In order to move forward, you will need to create an account. Your password must be eight characters long, contain at least one special character, one capital letter and a number. We look forward to discovering your talents. 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. 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 copilots, search assistants, document intelligence/generation, workflow automation agents, predictive models and decision-support tools, along with reusable AI components, prompt libraries, and solution patterns Implement RAG pipelines using enterprise data sources (SharePoint, data lake, document repositories, research systems, etc.) Build and maintain end-to-end AI pipelines: data ingestion, feature engineering, model training, evaluation, deployment, and monitoring Integrate LLMs via APIs and platforms (Azure OpenAI, OpenAI, Anthropic, AWS Bedrock) into business workflows Develop prompt engineering, grounding, and evaluation frameworks to improve accuracy and reliability Translate business use cases (e.g., medical affairs, regulatory, commercial, finance) into working AI prototypes and production apps Collaborate with Data Scientists to translate models into scalable production systems and with Product Owners and SMEs to refine requirements and success metrics Deploy and maintain AI solutions using cloud platforms and modern APIs Implement basic MLOps and LLMOps: versioning, monitoring, logging, performance tracking, observability, and cost optimization for AI workloads Implement guardrails to prevent data leakage, hallucinations, and misuse Integrate with identity, access control, and data-security platforms (RBAC, Purview, etc.) Ensure reliability, scalability, security of AI systems in production environments and that AI solutions follow data classification, privacy, and AI governance policies

Requirements

• Bachelor’s degree in Computer Science, Engineering, Data Science, or related field, with at least 8 years of experience in software engineering, data engineering, or applied AI engineering (An equivalent combination of experience and education may be considered)
• Strong proficiency in Python is required
• Experience building and deploying applications using LLM APIs and AI solutions in cloud environments (Azure, AWS, or GCP)
• Experience in Applied AI/ML & Prompt Engineering, Generative AI & LLM Integration, Enterprise Data Integration, API & Cloud Application Development, and Security-aware Engineering
• Hands-on experience with ML frameworks (PyTorch, TensorFlow, scikit-learn)
• Strong understanding of data engineering fundamentals, APIs, and distributed systems

Nice To Haves

• Experience with RAG architectures, vector databases, and semantic search is preferred
• Exposure to Azure OpenAI, Copilot Studio, LangChain, LlamaIndex, or similar frameworks is preferred
• Familiarity with MLOps platforms (MLflow, SageMaker, Azure ML, Databricks) is preferred
• Experience in regulated or data-sensitive environments (pharma, healthcare, finance) is preferred
• Familiarity with AI governance, responsible AI, model explainability, and data classification is preferred
• Experience building enterprise copilots or agentic AI solutions is preferred

Responsibilities

• Build AI applications such as copilots, search assistants, document intelligence/generation, workflow automation agents, predictive models and decision-support tools, along with reusable AI components, prompt libraries, and solution patterns
• Implement RAG pipelines using enterprise data sources (SharePoint, data lake, document repositories, research systems, etc.)
• Build and maintain end-to-end AI pipelines: data ingestion, feature engineering, model training, evaluation, deployment, and monitoring
• Integrate LLMs via APIs and platforms (Azure OpenAI, OpenAI, Anthropic, AWS Bedrock) into business workflows
• Develop prompt engineering, grounding, and evaluation frameworks to improve accuracy and reliability
• Translate business use cases (e.g., medical affairs, regulatory, commercial, finance) into working AI prototypes and production apps
• Collaborate with Data Scientists to translate models into scalable production systems and with Product Owners and SMEs to refine requirements and success metrics
• Deploy and maintain AI solutions using cloud platforms and modern APIs
• Implement basic MLOps and LLMOps: versioning, monitoring, logging, performance tracking, observability, and cost optimization for AI workloads
• Implement guardrails to prevent data leakage, hallucinations, and misuse
• Integrate with identity, access control, and data-security platforms (RBAC, Purview, etc.)
• Ensure reliability, scalability, security of AI systems in production environments and that AI solutions follow data classification, privacy, and AI governance policies

Benefits

• Full and comprehensive benefit program, including an Employee Stock Purchase Program and 401(k) matching.
• Opportunities to grow in a culture that prioritizes learning, development and progression through in-house programs and tuition reimbursement.
• A collaborative, innovative team that works as one to amplify your impact—on your career, the work you do and patients’ lives.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level