Jayden Carter
@jaydencarter1
Principal Machine Learning Architect building scalable enterprise generative AI platforms.
United StatesWhat I'm looking for
I’m a Principal Machine Learning Architect and Generative AI leader with 11 years designing and scaling enterprise-grade AI systems for demanding organizations. My focus is translating research-stage ideas into production AI infrastructure that ships reliably, holds up at scale, and delivers measurable business impact.
I specialize in large language model applications—generative AI pipelines, LLM platform design, and cloud-native MLOps—across AWS, Azure, and GCP. I lead cross-functional engineering teams and help shape AI strategy at the organizational level, operating at the intersection of rigorous system architecture and real-world engineering pragmatism.
Recently, I’ve served as principal architect for enterprise AI delivery, owning end-to-end design of LLM-powered applications, retrieval pipelines, and production model serving layers. I designed a multi-tenant generative AI platform that cut client deployment timelines from 14 weeks to under 3 weeks, and I led a team of 10 senior engineers across MLOps, NLP, and infrastructure.
Across my career, I’ve built and operationalized NLP and ML solutions—ranging from scalable RAG pipelines (GPT-4, LLaMA, BERT, LangChain, LlamaIndex) to production CI/CD and governance patterns with automated model lifecycle management. I’m especially proud of improving accuracy, throughput, and reliability—whether integrating inference into microservices or accelerating training and release cycles through repeatable pipelines.
Experience
Work history, roles, and key accomplishments
Served as principal architect for Palantir’s enterprise AI delivery practice, designing LLM-powered application and retrieval pipelines across classified and commercial environments. Led a team of 10 engineers to reduce multi-tenant generative AI deployment timelines from 14 weeks to under 3 weeks, cut ML model release cycles by 60%, and scale high-throughput RAG pipelines to process 1M+ documents
Designed and delivered ML models for LexisNexis contract analysis, improving automated legal review accuracy by 34%. Built scalable NLP and document-ingestion pipelines using AWS (500GB/day) and ensured 99.9% pipeline reliability, integrating ML inference into microservices for real-time legal research used by 400,000+ professionals.
Built predictive ML models for consumer behavior analysis and trend forecasting, processing 200M+ consumer signals weekly across social, e-commerce, and POS sources. Improved trend prediction accuracy by 28% through systematic experimentation with gradient boosting, neural networks, and statistical forecasting, and delivered executive-facing dashboards for brand strategy and campaign planning.
Expanded Clarifai’s platform beyond computer vision by designing and deploying NLP systems for text understanding, document classification, and entity extraction, creating a new enterprise product vertical. Built distributed training workflows in TensorFlow achieving 3x throughput improvement and deployed NLP inference as containerized REST APIs integrated into Clarifai’s core platform.
Education
Degrees, certifications, and relevant coursework
New Jersey Institute of Technology (NJIT)
Bachelor of Science, Computer Science
Earned a B.S. in Computer Science from NJIT.
Tech stack
Software and tools used professionally
Availability
Location
United StatesAuthorized to work in
Job categories
Skills
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