Senior Engineer 2, Applied Artificial Intelligence

Role Summary

Senior Engineer 2, Applied Artificial Intelligence builds and deploys AI solutions to support business workflows across Commercial, Regulatory, Quality, Finance, Operations, and Corporate functions. The role focuses on turning real business problems into AI applications—including copilots, retrieval-augmented generation (RAG) solutions, document generation, automation agents, predictive models, and decision-support tools. The position works 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. Hybrid role located in San Diego, CA.

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

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

Qualifications

• Required: 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)
• Required: Strong proficiency in Python is required
• Required: Experience building and deploying applications using LLM APIs and AI solutions in cloud environments (Azure, AWS, or GCP)
• Required: Experience in Applied AI/ML & Prompt Engineering, Generative AI & LLM Integration, Enterprise Data Integration, API & Cloud Application Development, and Security-aware Engineering
• Required: Hands-on experience with ML frameworks (PyTorch, TensorFlow, scikit-learn)
• Required: Strong understanding of data engineering fundamentals, APIs, and distributed systems
• Preferred: Experience with RAG architectures, vector databases, and semantic search
• Preferred: Exposure to Azure OpenAI, Copilot Studio, LangChain, LlamaIndex, or similar AI frameworks
• Preferred: Familiarity with MLOps platforms such as MLflow, SageMaker, Azure ML, or Databricks
• Preferred: Experience working in regulated or data‑sensitive environments (e.g., pharma, healthcare, finance)
• Preferred: Knowledge of AI governance, Responsible AI, model explainability, and data‑classification standards
• Preferred: Experience building enterprise copilots, agentic AI systems, or intelligent automation solutions