Key skills
PythonMachine learning frameworksCloud PlatformsClinical trial operationsSQLAPIs / microservicesMLOpsData pipelinesDeep learningNLP for clinical documentsGenerative AIKnowledge graphsContainerizationAIArtificial IntelligenceMachine LearningMLDeep LearningNLPLLMOpenAIRAGLangChainLlamaIndexAgenticPyTorchTensorFlowscikit-learnChromaPineconeWeaviateAnalyticsAWSAzureGCPKubernetesDockerCI/CD
About this role
BambooHR is seeking a highly skilled AI Engineer to design, build, and deploy next-generation AI solutions that transform clinical trial operations for Clinical Research Group (CRG). The role involves developing scalable AI solutions that integrate with modern clinical data ecosystems to improve data quality and reduce operational burden across various clinical processes.
Responsibilities:
- Build AI Solutions for Clinical Trial Operations
- Design and implement AI models and systems that improve clinical operations, including:
- Patient enrollment forecasting
- Protocol optimization
- Clinical site performance prediction
- Risk-based monitoring automation
- Clinical document intelligence
- Operational forecasting and simulation
- Example solutions include:
- AI-powered Clinical Trial Forecasting Suite (CTFS) for predicting enrollment timelines
- Agentic AI assistants supporting Clinical Research Associates (CRAs)
- Document intelligence systems extracting insights from protocols, investigator brochures, and regulatory submissions
- Develop Agentic AI Systems
- Design multi-agent AI workflows that automate complex clinical processes
- Example use cases include:
- AI agents reviewing monitoring reports and generating insights
- Automated protocol feasibility assessments
- Intelligent clinical data quality monitoring
- Study risk detection across trial sites
- Key capabilities include:
- Agent orchestration frameworks
- Retrieval-Augmented Generation (RAG) pipelines
- Autonomous workflow orchestration
- Guardrails and compliance monitoring
- Build Scalable AI Platforms
- Develop production-grade AI systems integrated with enterprise clinical platforms
- Key integrations include:
- Clinical Trial Management Systems (CTMS)
- Electronic Data Capture (EDC) systems
- ETMF document management platforms
- Safety and pharmacovigilance systems
- Real-world data sources
- Responsibilities include:
- Model deployment pipelines
- MLOps frameworks
- Scalable API development
- Cloud-native AI infrastructure
- Work on Advanced AI Models:
- Develop and deploy advanced AI capabilities including:
- Predictive ML models for clinical forecasting
- LLM-powered clinical copilots
- Knowledge graph–enabled RAG systems
- Simulation models for trial design optimization
- Techniques include:
- Deep learning
- Graph AI
- Time-series forecasting
- NLP for clinical documents
- Generative AI for clinical insights
- Ensure Compliance & Responsible AI
- Ensure AI systems meet life sciences regulatory requirements, including:
- GxP compliance
- Data privacy standards
- Auditability and traceability
- Model validation frameworks
- Work closely with clinical stakeholders to ensure solutions are trustworthy, compliant, and explainable
Requirements:
- Bachelor's or Master's degree in Computer Science, Artificial Intelligence / Machine Learning, Data Science, Bioinformatics Or a related technical field
- Programming: Python
- Programming: SQL
- Programming: APIs / microservices
- AI / Machine Learning: Machine learning frameworks (PyTorch, TensorFlow, Scikit-learn)
- AI / Machine Learning: LLM frameworks (LangChain, LlamaIndex, OpenAI APIs)
- AI / Machine Learning: Retrieval-Augmented Generation (RAG)
- Data Platforms: Data pipelines
- Data Platforms: Vector databases (Pinecone, Chroma, Weaviate)
- Data Platforms: Knowledge graphs / graph databases
- Cloud Platforms: AWS / Azure / GCP
- Cloud Platforms: Containerization (Docker, Kubernetes)
- MLOps: Model deployment
- MLOps: CI/CD for AI
- MLOps: Monitoring and observability
- Experience in life sciences or healthcare data environments, including familiarity with: Clinical trial operations
- Experience in life sciences or healthcare data environments, including familiarity with: CTMS / EDC / eTMF systems
- Experience in life sciences or healthcare data environments, including familiarity with: Clinical data standards (CDISC, SDTM, ADaM)
- Experience in life sciences or healthcare data environments, including familiarity with: Regulatory frameworks (GxP)
- Preferred experience with: Agentic AI architectures
- Preferred experience with: Digital twins or simulation systems
- Preferred experience with: Clinical data analytics
- Preferred experience with: Clinical trial optimization models
- Preferred experience with: Large enterprise platform development