Top Tech Jobs & Startup Jobs in Charleston, SC

Technical Architect role listed as a replacement position. The description provides no details about responsibilities, required technologies, qualifications, compensation, travel, or clearance requirements.

Lead a small AI engineering team while remaining hands-on in research and production software development. Build agentic capabilities for monitoring, guardrailing, evaluation, collaboration, and security. Perform exploratory data analysis, feature engineering, model fine-tuning, prototyping, and technical design. Translate AI research into scalable product features, set technical direction, mentor engineers, and deliver reliable production code.

Develop and optimize generative AI architectures for material and molecular discovery, including diffusion models, flow matching, and GFlowNets. Integrate reward signals from learned predictors and simulations, manage data ingestion and quality control, develop computational workflows, and deploy production-grade ML capabilities. Collaborate with ML researchers, computational scientists, engineering, product, and R&D teams. The role requires expertise in applied ML, scalable frameworks, cloud platforms, and material discovery, with preferred experience in active learning, Bayesian optimization, guided generation, and agentic or LLM methods.

Build production-ready agentic AI capabilities focused on monitoring, guardrails, evaluation, security, quality, and human-agent collaboration. Perform machine learning tasks including exploratory data analysis, feature engineering, and model fine-tuning. Translate research into scalable software, rapidly prototype features, collaborate cross-functionally, and explore emerging agentic technologies.

Leads GM Insurance’s regulatory and statistical reporting processes, including reporting inventories, filing calendars, data analysis, validation, reconciliation, controls, documentation, and submissions. Partners with Legal, Accounting, Actuarial, Claims, Product, Operations, Technology, and Data teams to resolve data issues, support audits and regulatory inquiries, improve data quality, and automate reporting processes. Requires advanced SQL, Python experience, insurance reporting knowledge, and strong analytical and stakeholder-management skills.

Apply advanced actuarial methods, predictive modeling, and data analytics to support reserving, ratemaking, and business decisions. Build and validate models, perform EDA, query and transform data, create visualizations, mentor junior analysts, lead projects, and collaborate with cross-functional teams to drive performance and perform periodic reserve reviews.

An 8-month Strategic Account Manager program preparing participants for dealer account management at GM Financial. Responsibilities include building dealer relationships, supporting automotive finance programs, analyzing dealer data, resolving credit and funding issues, identifying portfolio and compliance risks, and representing the GM Financial brand. The role requires travel, weekend availability during training, relocation after completion, and a valid driver's license.

The Senior IT QA Engineer owns quality strategy and outcomes across complex technology initiatives. Responsibilities include designing automated testing solutions, integrating quality verification into CI/CD pipelines, analyzing quality and production data, developing dashboards, managing risk, leading root-cause analysis, and influencing product, engineering, architecture, security, operations, and business stakeholders. The role requires technical contributions across web, mobile, API, data, cloud, and distributed systems, plus mentoring engineers and advancing quality engineering practices.

Lead product, cloud, application, infrastructure, and internal security across Hipp’s healthcare SaaS platform. Responsibilities include architecture reviews, threat modeling, vulnerability management, cloud and identity security, monitoring, incident response, AI-agent security, compliance controls, vendor risk, customer security reviews, and security mentorship.

Own system-level testing for Lance and LanceDB across correctness, reliability, scalability, performance, data integrity, fault recovery, upgrades, and compatibility. Build automated test frameworks, realistic workloads, fault-injection scenarios, performance and soak tests, and reproducible CI environments. Investigate failures using logs, metrics, traces, and source code, create minimal reproductions, and partner with engineers on fixes. Apply AI tools responsibly to accelerate test development, failure analysis, and debugging.

Own the strategy, roadmap, and success metrics for AI security capabilities across the Pathfinder platform. Serve as the internal expert on agentic AI threats, identity security, and emerging attack techniques. Define requirements for securing AI agents, MCP servers, and orchestration frameworks; conduct market and competitive research; lead design partner programs; coordinate cross-portfolio product work; and support analyst, customer, marketing, and pre-sales engagements.

Design and build production agentic AI systems and internal automation workflows across business functions. Responsibilities include prioritizing high-impact automation, developing multi-step and multi-agent workflows, integrating agent platforms, implementing human-in-the-loop controls, improving reliability and observability, evaluating performance, and shaping reusable AI engineering patterns. The role requires collaboration with nontechnical stakeholders and ownership of ambiguous problems from definition through measurable business impact.

Senior software engineering individual contributor responsible for owning systems or feature areas, making customer- and business-focused technical decisions, improving an agentic AI development framework, managing automated quality and defect reduction, leading design discussions, mentoring engineers, and driving cross-pod technical initiatives. The role requires hands-on software engineering leadership without direct people management, with emphasis on AI-assisted development, testing, safety, and organizational technical strategy.

Secure AI/ML models, data pipelines, and AI-enabled applications by conducting vulnerability assessments, threat modeling, incident response, and security reviews. Build guardrails, monitoring, detection, access controls, encryption, and data-segmentation measures. Partner with engineering and analytics teams throughout the AI development lifecycle, assess third-party AI tools, document remediation plans, and translate emerging threats such as prompt injection, data poisoning, jailbreaks, and model-supply-chain risks into practical mitigations.