Artificial Intelligence Jobs in Arlington, TX

Develop and execute enterprise AI and data strategies for clients, including AI value cases, operating models, talent strategies, technology roadmaps, responsible AI plans, and commercialization opportunities. Build trusted C-suite relationships, lead transformation workstreams, shape new business opportunities, and guide large-scale AI-enabled reinvention initiatives. The role requires consulting experience, strong program management and communication skills, leadership capability, and up to 80% travel.

Conduct research, experimentation, evaluation, and prototyping of AI, machine learning, big data, and serverless technologies for financial applications. Collaborate with statisticians, data engineers, developers, IT teams, and business partners to assess emerging technologies, develop solutions, and support their integration into applications. Present findings, evaluate AI outputs for accuracy and bias, and contribute to innovation initiatives in a financial technology lab.

Supports business process improvement by analyzing requirements and workflows, preparing reports, creating system documentation and workflow diagrams, assisting users, and contributing to special projects. The intern collaborates cross-functionally to improve operational efficiency and productivity while gaining experience in business analysis, data analysis, problem-solving, and responsible use of AI tools.

Design, build, deploy, and operate production AI-native systems spanning applications, APIs, data, cloud infrastructure, DevSecOps, security, observability, and agentic AI. Lead architecture, client engagements, multidisciplinary engineering teams, and technical delivery from ambiguous requirements through production. Build cloud-native platforms, CI/CD pipelines, secure data and context architectures, AI integrations, agent orchestration, evaluations, and production safeguards. Establish reusable engineering standards, tooling, and practices for enterprise AI adoption.

Designs, deploys, and operates AI and HPC infrastructure across on-premises, cloud, and hybrid environments. Manages GPU and accelerated-computing clusters, Kubernetes and Slurm orchestration, AI serving platforms, networking, storage, observability, and automation. Builds governed agentic AI infrastructure, MCP integrations, APIs, and platform services. Uses NVIDIA tooling and inference frameworks to optimize large-scale training, inference, simulations, reliability, energy efficiency, and cost. Provides technical guidance, troubleshooting, performance validation, architecture documentation, and operational runbooks. Travel may range from 25% to 100%.

Lead architecture and delivery of enterprise Salesforce Data Cloud and agentic AI solutions for life sciences clients. Design data models, identity resolution, ingestion, segmentation, CRM integrations, Agentforce/Voice AI workflows, and governance. Drive pre-sale solutioning, design reviews, mentor architects, and contribute reusable patterns and accelerators across programs.

Provides strategic cybersecurity architecture leadership across applications, data, networks, cloud, SaaS, and emerging AI technologies. Develops security requirements, designs secure technical architectures, assesses risk, improves security frameworks, supports SDLC activities, and supervises cybersecurity testing. Partners with business and executive stakeholders to enable strategic initiatives while strengthening security controls and resolving complex technical challenges.

Evaluate AI coding-agent interactions for usefulness, correctness, engineering judgment, reasoning quality, and developer trust. Provide clear, opinionated feedback, distinguish response-quality levels, and help define standards for excellent interactions with tools such as OpenAI Codex, Claude Code, and Cursor. This contract role requires staff- or principal-level software engineering experience and familiarity with modern AI-assisted development workflows.

Develop and maintain scalable cloud-native web, mobile, API, and data products; migrate services to public cloud; implement CI/CD and test automation; contribute to Agile teams, troubleshoot production issues, and promote secure, high-quality software practices.

The Staff Level AI Engineer will innovate applied AI methods, enhance user experience through AI, and architect AI infrastructure for a SaaS platform, collaborating across product and engineering teams to drive business value.

Pioneering machine learning solutions for the defense sector, developing and deploying production-grade ML models, tackling complex data challenges, and collaborating with engineers.