Associate Director, Full Stack Clinical Platform Engineer

Job Overview The Full Stack Clinical Platform Engineer will design, implement, and operate production-grade Generative AI and Machine Learning solutions that power Kardigan's Global Development Digital Transformation initiative. This role sits at the intersection of data engineering and applied AI-partnering closely with Clinical Operations, Data Management, Regulatory, and IT to design, build, and maintain the modern data platforms and AI-enabled pipelines that underpin the transformation program. This is 4 -day on-site position (M-Th) Candidates must have experience in Biotech/Pharma/Healthcare The ideal candidate brings deep expertise in Biotech/Pharma or Healthcare clinical data infrastructure and modern data engineering, combined with hands-on experience deploying machine learning solutions in regulated life sciences environments. This role will act as both a technical authority and strategic liaison between transformation projects and enterprise IT, ensuring that solutions are scalable, compliant, and aligned with evolving regulatory and data standards. Key Responsibilities 3 Translate ambiguous clinical and operational problems into well-scoped AI solutions, from problem framing and data assessment through prototype, validation, and production deployment. 3 Design and implement MLOps/LLMOps pipelines to deploy, monitor, and manage large language models in production environments, following software engineering best practices 3 Collaborate with data scientists to deploy and/or fine-tune high-performing Generative AI models, and apply modern techniques from relevant published work where appropriate. 3 Develop scalable and robust data and ML pipelines for ingestion, preprocessing, validation, training, evaluation, and model deployment across the clinical development ecosystem. 3 Evaluate and recommend AI tools and frameworks to meet clinical and operational requirements, including decisions around retrieval-augmented generation (RAG), vector databases, embedding models, and LLM providers, balancing compliance, performance, and cost. 3 Develop, deploy, and maintain robust data pipelines for structured and unstructured clinical data, integrating internal systems with CRO and external partner data sources, and ensuring end-to-end data integrity and traceability. 3 Identify and implement opportunities to increase data interoperability and standardization across Global Development systems and other business units, reducing manual effort and accelerating data availability for clinical programs. 3 Develop and implement automated quality monitoring pipelines for both internal and CRO-sourced clinical data, surfacing quality metrics and triggering corrective workflows in alignment with the study's Medical Monitoring Plan. 3 Ensure all data solutions comply with applicable regulatory frameworks including HIPAA, GDPR, and 21 CFR Part 11, and contribute to data governance strategy, data lineage documentation, and audit-readiness. 3 Maintain and manage code repositories (e.g., Bitbucket, GitHub) with clean, well-documented, version-controlled code; uphold engineering best practices including code review, testing, and CI/CD pipelines. Qualifications 3 Advanced degree in computer science, biomedical informatics, statistics, or a closely related field required; PhD with 6+ years of relevant experience or MS with 10+ years of relevant experience strongly preferred. 3 Minimum of 5-7 years of experience designing, implementing, and leading data engineering solutions in life sciences or healthcare, with demonstrated accountability for end-to-end delivery is required. 3 Demonstrated expertise in designing and maintaining clinical or biomedical data infrastructure, including data lake and warehouse architectures optimized for regulatory-grade clinical data. 3 Expertise in modern cloud data platforms (Snowflake, Databricks, Redshift, BigQuery) and proficiency in Python, SQL, R, and related programming languages. 3 Proficiency in cloud architecture (AWS, Azure, or GCP) and DevOps practices including CI/CD, containerization (Docker/Kubernetes), and infrastructure-as-code; relevant certifications a plus. 3 Demonstrated experience building, scaling, and maintaining pipelines for structured and unstructured data, with the ability to integrate pipelines across the enterprise. 3 Deep knowledge of regulatory frameworks (HIPAA, GDPR, 21 CFR Part 11) and clinical data standards (CDISC, HL7, FHIR), with experience applying them in regulated development environments. 3 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. 3 Strong scientific communication skills, with the ability to translate complex technical architectures and outputs into clear strategic recommendations for non-technical clinical and executive stakeholders. 3 Experience with data governance frameworks, data quality tooling, and metadata management practices in clinical or regulated settings. Ideal Candidate Trail 3 A builder at heart - someone who moves fluidly from whiteboard to working solution, comfortable owning the full arc from idea through deployed product. 3 Able to assess a clinical or operational challenge and independently determine whether and how AI can meaningfully solve it - not just implement what's handed to them. 3 A natural cross-functional collaborator who earns the trust of clinical, operations, regulatory, and IT stakeholders alike-comfortable operating as both a technical lead and a strategic partner. 3 Solutions-oriented and innovation-driven, with the confidence to constructively challenge legacy thinking and the pragmatism to deliver within the constraints of a regulated environment. 3 Thrives in ambiguity and fast-moving environments, able to balance long-horizon architectural thinking with near-term delivery commitments across multiple concurrent transformation workstreams. 3 A proactive learner who actively monitors advances in AI/ML, data engineering, and clinical informatics and brings external insights back to accelerate the program's evolution. Exact Compensation may vary based on skills, experience and location. Pay range $234,000-$305,000 USD