Artificial Intelligence Jobs in Richardson, TX

Lead and grow an engineering team building high-performance, low-latency communications platforms on AWS (Amazon Connect). Drive technical roadmap, performance and capacity planning, reliability, cross-functional delivery, metrics-driven prioritization, hiring and coaching, and modernization of cloud-based services and analytics pipelines.

Supports consulting clients in identifying, prioritizing, and implementing Generative AI solutions. Responsibilities include facilitating workshops, analyzing requirements and processes, supporting proof-of-concepts, exploring data, developing documentation, testing solutions, tracking project plans, translating business needs into technical specifications, and delivering GenAI training. The role emphasizes Microsoft AI technologies, prompt engineering, Agile and SDLC practices, continuous AI learning, and client collaboration.

Lead RTL development and microarchitecture for an AI-enabled RISC-V CPU core. Responsibilities include designing vector and matrix-processing engines, optimizing power, performance, area, and timing, developing detailed specifications, supporting functional verification, debugging simulations, and partnering on physical design validation. The role requires expertise in high-performance processor architecture, datapath design, vector and matrix processing, and hardware design languages.

Lead and scale integration engineering for pharmacy and consumer wellness lines by embedding AIOps, AI/ML and automation across cloud-native platforms (Azure/AWS/GCP). Own modernization, API orchestration, CI/CD, testing and ML model validation; drive roadmaps, budgeting, vendor partnerships, hiring, and OKRs to ensure resilient, cost‑optimized, high‑quality delivery and proactive incident management.

Lead assessment and secure adoption of AI/ML and GenAI systems for clients. Design security patterns and controls, perform assessments and pen tests, prototype AI-enabled security capabilities, ensure compliance (PII, privacy, regulatory), document protocols, and collaborate with cross-functional teams to productionize scalable AI-security solutions.

Design, develop, test, and support scalable AI/ML-powered enterprise applications and microservices. Implement LLMs, Generative AI, and RAG solutions; build RESTful APIs, automated tests, CI/CD pipelines; troubleshoot production issues and collaborate in Agile teams.

Support the Corporate AI team by gathering requirements, mapping processes, evaluating AI tools, managing intake and KPIs, and producing clear documentation to turn ideas into measurable AI solutions.

As a Senior QA Automation Engineer, you will design automation frameworks, test AI features, and collaborate with developers to ensure high code quality.

Lead end-to-end AI/ML architecture across the organization: define reference architectures, design patterns, and governance; architect data pipelines, feature stores, deployment, monitoring, and MLOps; align with security, infrastructure, and business domains; drive adoption of modern AI capabilities (LLMs, generative AI, vector DBs); mentor teams and communicate technical strategy to stakeholders.

Lead delivery of core platform capabilities from ideation to launch for an enterprise-scale agentic/AI platform. Author specs, prioritize work, run discovery with customers, partner with engineering, UX, and GTM teams, track OKRs, and ensure successful customer enablement. Operate in a fast-moving, AI-focused product environment and adapt plans as business priorities change.

Lead a forward‑deployed AI solutions team to translate enterprise AI capabilities into business outcomes. Partner with executives to co‑design and deliver priority use cases, drive AI productivity tool adoption, establish repeatable AI solution patterns, and feed learnings back into the AI platform and governance. Balance rapid delivery with enterprise rigor while coaching teams and managing stakeholders.

Act as primary technical partner for enterprise customers, troubleshoot complex issues across contact center platforms and integrations, coordinate escalations, leverage AI tools for insights, drive customer outcomes, share feedback with Product/AI teams, mentor peers, and shape AI-augmented support processes.

Develop, validate, and improve machine learning models (regression, classification, forecasting, optimization). Prepare data, perform feature engineering, train and evaluate models, and support deployment with Data/ML Engineering. Communicate results, document methods, and collaborate on experiments and cross-functional initiatives.