Responsibilities
- Build AI applications such as copilots, search assistants, document intelligence/generation, workflow automation agents, predictive models and decision-support tools.
- 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
- Collaborate with Product Owners and SMEs to refine requirements and success metrics
- Build reusable **AI components, prompt libraries, and solution patterns
- Deploy and maintain AI solutions using cloud platforms and modern APIs
- Implement basic MLOps and LLMOps: versioning, monitoring, logging, performance tracking
- Integrate with identity, access control, and data-security platforms (RBAC, Purview, etc.)
- Implement logging, observability, performance tracking, and cost optimization for AI workloads
- Ensure reliability, scalability, and security of AI systems in production environments
- Ensure AI solutions follow data classification, privacy, and AI governance policies
- Support documentation for model usage, data sources, and risk assessments
- Implement guardrails to prevent data leakage, hallucinations, and misuse
Preferred qualifications
- Experience with RAG architectures, vector databases, and semantic search
- Exposure to Azure OpenAI, Copilot Studio, LangChain, LlamaIndex, or similar frameworks
- Familiarity with MLOps platforms (MLflow, SageMaker, Azure ML, Databricks)
- Experience in regulated or data-sensitive environments (pharma, healthcare, finance)
- Familiarity with AI governance, responsible AI, model explainability, and data classification
- Experience building **enterprise copilots or agentic AI solutions
- Applied AI/ML & Prompt Engineering
- Generative AI & LLM Integration
- Enterprise Data Integration
- API & Cloud Application Development
- Security-aware Engineering
- Business Problem Solving & Systems Thinking
- Stakeholder Communication & Collaboration
Benefits
- 401(k)
- 401(k) matching
- Dental insurance
- Health insurance
- Paid time off
- Vision insurance
- Bachelor's (Preferred)
- AI: 3 years (Required)
- AI models: 3 years (Required)
- Python: 3 years (Required)
- ML frameworks: 3 years (Required)
- LLM APIs: 3 years (Required)
- Cloud: 3 years (Required)
- RAG: 2 years (Required)
- Azure OpenAI: 1 year (Preferred)
- San Diego, CA 92130 (Required)
Tags & focus areas
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