Contact: Sabrina Garcia -
No 3rd party candidates
Position Overview
The Full Stack Clinical Platform Engineer, Digital Transformation will design, implement, and operate production-grade Generative AI and Machine Learning solutions supporting the Global Development Digital Transformation initiative.
This role combines data engineering and applied AI, partnering with Clinical Operations, Data Management, Regulatory, and IT to build modern data platforms and AI-enabled pipelines. The ideal candidate has deep clinical data infrastructure expertise and hands-on experience deploying ML solutions in regulated life sciences environments.
Location: New Jersey preferred. Remote may be considered for highly qualified candidates.
Key Responsibilities
- Translate clinical and operational challenges into AI solutions from problem definition and data assessment through validation and production deployment.
- Design MLOps/LLMOps pipelines to deploy, monitor, and manage large language models in production.
- Partner with data scientists to deploy and fine-tune Generative AI models.
- Build scalable data and ML pipelines for ingestion, preprocessing, validation, training, evaluation, and deployment.
- Evaluate AI tools and frameworks including RAG, vector databases, embedding models, and LLM providers, balancing compliance, performance, and cost.
- Build pipelines for structured and unstructured clinical data, integrating internal, CRO, and external partner sources while ensuring data integrity and traceability.
- Improve data interoperability and standardization across Global Development systems to reduce manual effort and accelerate data availability.
- Implement automated quality monitoring for internal and CRO-sourced clinical data.
- Ensure compliance with HIPAA, GDPR, and 21 CFR Part 11 while supporting data governance, lineage, and audit readiness.
- Maintain clean, version-controlled code and engineering best practices including testing, code review, and CI/CD.
Qualifications
- Advanced degree in computer science, biomedical informatics, statistics, or related field required; PhD with 6+ years or MS with 10+ years of relevant experience strongly preferred.
- 5–7+ years designing and leading data engineering solutions in life sciences or healthcare.
- Expertise in clinical or biomedical data infrastructure, including data lake and warehouse architectures for regulatory-grade clinical data.
- Experience with Snowflake, Databricks, Redshift, or BigQuery and proficiency in Python, SQL, R, or related languages.
- Proficiency in AWS, Azure, or GCP and DevOps practices including CI/CD, Docker/Kubernetes, and infrastructure-as-code.
- Experience building and scaling structured and unstructured data pipelines.
- Strong knowledge of HIPAA, GDPR, 21 CFR Part 11 and clinical data standards including CDISC, HL7, and FHIR.
- Hands-on experience with ML pipelines and clinical AI/ML applications such as NLP, anomaly detection, or predictive modeling.
- Experience with data governance, data quality, and metadata management in regulated environments.
- Strong communication skills with the ability to translate complex technical concepts for clinical, business, and executive stakeholders.
Ideal Candidate Profile
- Hands-on builder who can own solutions from concept through production.
- Able to assess clinical and operational challenges and determine where AI can provide meaningful value.
- Strong cross-functional collaborator who can serve as both technical lead and strategic partner.
- Innovative and solutions-oriented while comfortable working within regulated environments.
- Able to navigate ambiguity and balance long-term architecture with near-term delivery across multiple initiatives.
- Stays current on AI/ML, data engineering, and clinical informatics and applies relevant advances to the program.
