Associate Director, Full Stack Clinical Platform Engineer

Kardigan•Princeton, NJ

5d•$234,000 - $305,000•Onsite

About The Position

The Full Stack Clinical Platform Engineer will design, implement, and operate production-grade Generative AI and Machine Learning solutions supporting Kardigan’s Global Development Digital Transformation initiative. Working at the intersection of data engineering and applied AI, the role partners with Clinical Operations, Data Management, Regulatory, and IT to build and maintain modern data platforms and AI-enabled pipelines. The position also serves as a technical authority and strategic liaison between transformation projects and enterprise IT, ensuring solutions are scalable, compliant, and aligned with regulatory and data standards. Candidates must have Biotech, Pharma, or Healthcare experience. This is a four-day on-site position, Monday through Thursday.

Requirements

Advanced degree in computer science, biomedical informatics, statistics, or a closely related field.
At least 5–7 years designing, implementing, and leading data engineering solutions in life sciences or healthcare, with accountability for end-to-end delivery.
Experience in Biotech, Pharma, or Healthcare.
Expertise designing and maintaining clinical or biomedical data infrastructure, including data lake and warehouse architectures for regulatory-grade clinical data.
Expertise with modern cloud data platforms such as Snowflake, Databricks, Redshift, or BigQuery, and proficiency in Python, SQL, R, and related programming languages.
Proficiency in cloud architecture (AWS, Azure, or GCP) and DevOps practices, including CI/CD, Docker/Kubernetes containerization, and infrastructure-as-code.
Demonstrated experience building, scaling, and maintaining structured and unstructured data pipelines and integrating them across an enterprise.
Deep knowledge of HIPAA, GDPR, and 21 CFR Part 11, and clinical data standards such as CDISC, HL7, and FHIR, with experience applying them in regulated development environments.
Hands-on experience developing or integrating machine learning pipelines and working with clinical AI/ML applications such as natural language processing, anomaly detection, or predictive modeling.
Strong scientific communication skills and ability to translate technical architectures and outputs into strategic recommendations for non-technical clinical and executive stakeholders.
Experience with data governance frameworks, data quality tooling, and metadata management in clinical or regulated settings.

Nice To Haves

PhD with 6+ years of relevant experience or MS with 10+ years of relevant experience is strongly preferred.
Relevant cloud or other certifications are a plus.
Experience deploying and/or fine-tuning Generative AI models and applying modern techniques from relevant published work.
A builder who can own work from idea through deployed product and independently assess whether AI can solve a clinical or operational challenge.
Cross-functional collaboration skills and ability to act as both a technical lead and strategic partner.
Ability to challenge legacy approaches pragmatically and deliver within regulated-environment constraints.
Comfort working in ambiguity and balancing architecture with near-term delivery across concurrent transformation workstreams.
Proactive learning about AI/ML, data engineering, and clinical informatics.

Responsibilities

Translate clinical and operational problems into scoped AI solutions, from problem framing and data assessment through prototyping, validation, and production deployment.
Design and implement MLOps/LLMOps pipelines to deploy, monitor, and manage production large language models using software engineering best practices.
Collaborate with data scientists to deploy or fine-tune Generative AI models and apply relevant techniques from published work.
Develop scalable data and ML pipelines for ingestion, preprocessing, validation, training, evaluation, and deployment across the clinical development ecosystem.
Evaluate and recommend AI tools and frameworks, including RAG approaches, 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 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 for clinical programs.
Implement automated quality-monitoring pipelines for internal and CRO-sourced clinical data, surface quality metrics, and trigger corrective workflows aligned with the study’s Medical Monitoring Plan.
Ensure data solutions comply with HIPAA, GDPR, and 21 CFR Part 11, and contribute to data governance, lineage documentation, and audit readiness.
Maintain code repositories and uphold engineering practices including documentation, version control, code review, testing, and CI/CD.