Sr Mngr, Full Stack Clinical Platform Engineer
Job Description
Description
Remote\n
\n
\nOur client seeks a Full Stack Clinical Platform Engineer to design, implement, and operate production-grade Generative AI and Machine Learning solutions that support Global Development Digital Transformation. The role sits at the intersection of data engineering and applied AI. It partners with Clinical Operations, Data Management, Regulatory, and IT to build and maintain modern data platforms and AI-enabled pipelines that underpin the transformation program. The position requires expertise in clinical data infrastructure, modern data engineering, and deployment of machine learning solutions in regulated life sciences environments. The engineer will act as a technical authority and strategic liaison to ensure scalable, compliant solutions aligned with evolving regulatory and data standards.\n
\n
We can facilitate w2 and corp-to-corp consultants. For our w2 consultants, we offer a great benefits package that includes Medical, Dental, and Vision benefits, 401k with company matching, and life insurance.
Rate: $90.00 to $110.00/hr. w2\n
\n
Responsibilities
\n
- \n
- Translate ambiguous clinical and operational problems into well-scoped AI solutions from problem framing and data assessment through prototype, validation, and production deployment.
- Design and implement MLOps/LLMOps pipelines to deploy, monitor, and manage large language models in production environments following software engineering best practices.
- Collaborate with data scientists to deploy and fine-tune Generative AI models and apply modern techniques from relevant published work where appropriate.
- Develop scalable data and ML pipelines for ingestion, preprocessing, validation, training, evaluation, and model deployment across the clinical development ecosystem.
- Evaluate and recommend AI tools and frameworks including RAG, vector databases, embedding models, and LLM providers, balancing compliance, performance, and cost.
- Develop, deploy, and maintain pipelines for structured and unstructured clinical data, integrating internal systems with CRO and external partner data sources while ensuring data integrity and traceability.
- Increase data interoperability and standardization across Global Development systems and other business units to reduce manual effort and accelerate data availability.
- Implement automated quality monitoring pipelines for internal and CRO-sourced clinical data, surfacing metrics and triggering corrective workflows aligned with Medical Monitoring Plans.
- Ensure compliance with HIPAA, GDPR, and 21 CFR Part 11. Contribute to data governance strategy, data lineage documentation, and audit-readiness.
- Maintain code repositories with clean, well-documented, version-controlled code. Uphold engineering best practices including code review, testing, and CI/CD pipelines.
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
Experience Requirements
\n
- \n
- Advanced degree in computer science, biomedical informatics, statistics, or a related field. MS with 6+ years of relevant experience preferred.
- 5–7 years of experience leading data engineering solutions in life sciences or healthcare with end-to-end delivery accountability.
- Expertise in clinical or biomedical data infrastructure including data lakes and warehouses optimized for regulatory-grade clinical data.
- Experience with modern cloud data platforms such as Snowflake, Databricks, Redshift, or BigQuery. Proficiency in Python, SQL, and R.
- Proficiency in cloud architecture on AWS, Azure, or GCP and DevOps practices including CI/CD, containerization with Docker or Kubernetes, and infrastructure-as-code.
- Experience building and maintaining pipelines for structured and unstructured data with enterprise integration.
- Deep knowledge of HIPAA, GDPR, 21 CFR Part 11, and clinical data standards such as CDISC, HL7, and FHIR.
- Hands-on experience with machine learning pipelines and clinical AI/ML applications including NLP, anomaly detection, or predictive modeling.
- Strong communication skills to translate technical architectures and outputs into clear recommendations for non-technical stakeholders.
- Experience with data governance frameworks, data quality tooling, and metadata management in regulated settings.
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
Education Requirements
Advanced degree in computer science, biomedical informatics, statistics, or a related field. MS with 6+ years of relevant experience preferred.