Top Senior Machine Learning Jobs in Charleston, SC

Design, build, and productionize generative AI solutions: advanced Python development, LLM prompt engineering and fine-tuning, agent frameworks, RAG pipelines with vector DBs, API and cloud deployment, MLOps/LLMOps, model evaluation and Responsible AI practices, and translate business needs into scalable AI applications.

Design, build, and scale production Generative AI applications: multi-agent systems, advanced RAG pipelines, LLM integration and fine-tuning, scalable FastAPI backends, vector search, monitoring, and Dockerized deployments.

Design, build, and deploy production-grade ML and AI systems end-to-end, combining classical ML, deep learning, and LLM/agentic integrations. Implement MLOps, monitoring, CI/CD, model serving, vector search/RAG pipelines, and scalable APIs; mentor engineers and drive engineering best practices.

Design, build, and deploy production Generative AI applications: orchestrate multi-agent workflows, implement advanced RAG with vector DBs, integrate and fine-tune LLMs, develop scalable FastAPI/async backends, containerize with Docker, and optimize GenAI systems for latency, cost, and reliability.

Design, build, and scale production Generative AI applications including multi-agent systems, advanced RAG pipelines, and local/frontier LLM deployments. Implement high-performance backends with FastAPI and async Python, fine-tune and quantize models, integrate vector databases and retrieval systems, and deploy/monitor containerized AI services for reliability, latency, and cost optimization.

Build and scale production Generative AI applications: design multi-agent systems and LangGraph workflows, implement advanced RAG with LlamaIndex and vector DBs, integrate and fine-tune various LLMs, and deploy containerized FastAPI services with monitoring, CI/CD, and performance/cost optimizations.

Design, develop, and maintain scalable ML and AI platform infrastructure for model training, deployment, feature engineering, candidate generation, agent deployment, and observability. Collaborate with data scientists and engineers, support platform operations, drive improvements, codify best practices, and influence platform investments to enable production ML systems at scale.

The Senior Analyst in Predictive Analytics and Machine Learning develops and maintains statistical models to support business decisions, monitors model performance, and collaborates with stakeholders across the company.

Lead the end-to-end ML execution: build and operate data pipelines, training and evaluation systems, and scalable inference. Fine-tune and optimize large models (LoRA/QLoRA/SFT/DPO/distillation), ensure GPU efficiency, monitor production quality, collaborate with product and application engineering, and rapidly ship reliable, safe ML solutions that translate research into production.

Design, train and validate supervised ML models on multi-billion claim records to score fraud risk; engineer features in-warehouse; deploy and monitor models in production; provide human-readable rationale and claim-level evidence; collaborate with subject-matter experts and stakeholders to ensure actionable, explainable outputs.

Lead production-grade ML systems: build data pipelines, training and evaluation workflows, and scalable GPU inference. Fine-tune and adapt large models (LoRA, QLoRA, SFT, DPO, distillation), optimize deployments for latency/cost/reliability, and collaborate with product and engineering to ship robust, observable model-driven features.

Build, deploy, and maintain production-grade ML systems across the full model lifecycle. Translate business goals into ML solutions, monitor model performance and drift, perform analyses and experiments, and collaborate with data engineers, product, and domain experts to improve risk assessment and underwriting outcomes.

Lead relevance evaluation and quality improvements by fine-tuning LLMs, building low-latency reranking and filtering systems, scaling LLM deployments, creating automated monitoring pipelines and agentic insights, and validating impact via A/B tests while collaborating with cross-functional teams.

Lead and grow a team of ML engineers building production ML systems for Fetch's Ad Platform. Translate product strategy into roadmaps, guide design and implementation of models for ranking, targeting, bidding and forecasting, set metrics and experimentation, enforce architectural and operational standards, manage trade-offs across performance and reliability, and drive cross-functional alignment to improve advertiser and member outcomes.

Lead and grow a distributed ML engineering team that runs experiments, ships production models, and partners with Risk and platform teams. Balance research bets and delivery, enforce rigorous evaluation and reproducibility, mentor engineers, own roadmap trade-offs, and represent results to stakeholders while improving model performance and production reliability.

Build and maintain backend services and infrastructure for ML and generative AI features. Develop scalable, resilient, high-performance Python services (async frameworks, streaming), deploy to Kubernetes, integrate RAG and LLM orchestration, instrument LLM observability, and collaborate cross-functionally to deliver production-ready AI products.

The Senior Consultant in AI will design, build, and deliver AI/ML solutions on Azure, engage with clients, and develop project components independently.

The Senior Machine Learning Engineer will manage the full ML lifecycle, collaborating with teams to develop scalable systems that improve product decisions and empower monetization strategies.

Lead design, deployment, and monitoring of predictive models for credit risk, collections, and operations. Drive adoption of modern ML and MLOps, mentor data scientists, define strategic roadmap, advise senior leadership, and ensure model governance and regulatory compliance.

Lead the end-to-end machine learning lifecycle for AI products, managing data pipelines and model development to optimize performance and accuracy in production.

As a Lead Machine Learning Engineer at Root, you'll build scalable ML systems, partner with data scientists, and improve operational practices for customer lifetime value modeling.

Lead research on humanistic AI for autonomous systems. Collaborate with teams, design experiments, and publish findings in top-tier publications.

Design, build, and ship AI-powered observability features using LLMs and agentic workflows. Rapidly prototype, iterate with real users, integrate AI into alerting, runbooks, and developer workflows, and ensure solutions are scalable, maintainable, and production-ready while collaborating cross-functionally.

Lead and scale Vibrant's data science, ML, and AI function: set strategy and governance, oversee applied research and production ML (especially NLP), ensure HIPAA-compliant deployments, partner with execs and engineering, recruit and mentor teams, and deliver measurable outcomes for crisis services and enterprise initiatives.

Lead a Machine Learning engineering team owning mission, execution, morale, and code quality. Partner with product to deliver roadmap, remove roadblocks, improve processes, coach and grow engineers, and drive development, deployment, and innovation of ML models for recommender, search, and matching systems.