Senior Engineer 2, Applied Artificial Intelligence

Halozyme • San Diego, CA

22h • $116 - $162

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.

Requirements

• 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
• 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 pipelines, vector databases, and semantic search systems
• Exposure to Azure OpenAI, Copilot Studio, LangChain, LlamaIndex, or similar AI frameworks
• Familiarity with MLOps platforms such as MLflow, SageMaker, Azure ML, or Databricks
• Experience working in regulated or data‑sensitive environments (e.g., pharma, healthcare, finance)
• Knowledge of AI governance, Responsible AI, model explainability, and data‑classification standards
• Experience building enterprise copilots, agentic AI systems, or intelligent automation solutions
• 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 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

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