Top Artificial Intelligence Jobs in Des Moines, IA

Design and scale platform infrastructure, backend services, developer tooling, CI/CD pipelines, and secure cloud-native systems. Improve developer experience, reliability, observability, and incident response while establishing security and compliance controls. Partner with engineering and product leaders to shape architecture and platform strategy, mentor engineers, and support customer-facing logistics projects. The role uses Python, AWS, Terraform, containers, orchestration, event-driven architectures, and relational databases.

Leads enterprise quality engineering and test automation strategy across UI, API, integration, performance, and data layers. Architects Java-based automation frameworks, integrates quality gates into CI/CD pipelines, and drives AI/LLM adoption for test generation, debugging, self-healing, test data creation, and defect analysis. Establishes ETL and data validation practices, governs DevOps quality standards, mentors QA teams, and collaborates with Engineering, DevOps, Data Engineering, and Product stakeholders to improve quality maturity and delivery predictability.

Manage and optimize client outbound campaigns, source and verify lead data, execute onboarding-driven strategies, run internal outbound for growth, and maintain email deliverability and domain health using outreach automation tools.

Generate qualified sales pipeline through targeted, multichannel outbound prospecting. Research accounts, create personalized messaging and account plans, manage Salesforce data, partner with Marketing on ABM campaigns, and use AI tools, agents, and automation to improve prospecting efficiency. Track pipeline and performance while helping build scalable GTM processes in a startup environment.

Lead enterprise payer and MSO sales from prospecting through signed agreements. Sell AI value propositions to health plans and value-based care organizations, manage complex 9–18 month procurement cycles, conduct proof-of-performance deployments, and convert pilots into paid engagements. Own territory strategy, pipeline accuracy, executive relationships, and commercial feedback to product and clinical teams.

Provides technical customer support by triaging and resolving issues involving RelationalAI, Snowflake, Azure, databases, and graph technologies. Troubleshoots logs, stack traces, performance problems, environments, and client applications; communicates with technical and executive stakeholders; collaborates with Sales, Field Engineering, Product Management, and Product Engineering; and improves support processes, tools, products, and customer experience.

The Content & Prompt Operations Specialist manages AI-assisted content workflows, ensuring accuracy and engagement in content produced. Responsibilities include writing prompts, drafting content, proofreading, and improving AI outputs while following structured processes.

Own a core product domain within Clarium’s healthcare supply chain platform, leading discovery, requirements, delivery, iteration, and product direction. Partner closely with engineering, design, data, sales, marketing, and customer success while engaging healthcare customers and stakeholders. Remove roadblocks, make tradeoffs, and establish product practices in a fast-growing startup. The role requires healthcare technology experience, enterprise workflow and integration expertise, and familiarity with driving change in large health systems. Epic integrations and AI-powered product experience are preferred.

Design, mesh, simulate, and post-process maritime hull hydrodynamics at scale. Build simulation data pipelines for AI foundation models, advise AI researchers on marine CFD phenomena, collaborate with clients on validation datasets and workflows, and lead technical conversations bridging marine engineering and AI. The role requires expertise in ship hydrodynamics, free-surface flows, solver frameworks, hull design, CAD parameterization, and numerical methods.

Build agentic LLM systems that acquire, clean, validate, curate, and document large-scale biomedical datasets for multimodal model training. Develop reliable agent architectures, tooling, evaluation harnesses, quality-control workflows, and reproducible data pipelines. Collaborate with ML scientists, monitor distributed production pipelines, diagnose failures, and implement secure sandboxed execution with restricted access and audit logging.