Artificial Intelligence Jobs in Plano, TX

Design, build, and maintain secure cloud-based frameworks to host and deploy ML models using Python and AWS. Collaborate with data scientists and platform teams to productionize models, apply MLOps/LLMOps practices, use AI-assisted development tools, ensure CI/CD, resiliency, and operational stability, and contribute to engineering communities and architecture/design artifacts.

Leads enterprise SEO and AI discovery strategy across search engines, answer engines, and AI platforms. Owns roadmaps, technical discoverability, content architecture, entity optimization, structured data, KPI reporting, experimentation, and competitive analysis. Partners with marketing, product, UX, engineering, and content teams to improve visibility, citations, recommendations, traffic, engagement, and conversions. Influences backlogs, communicates executive recommendations, and coaches SEO and content contributors.

Lead GEICO’s IVR and conversational AI product strategy, roadmap, and lifecycle. Drive voice experience design, self-service adoption, containment, analytics, and customer satisfaction through cross-functional collaboration with engineering, design, operations, and business teams. Responsibilities include market research, feature prioritization, platform architecture influence, IVR performance optimization, analytics implementation, and integration of NLP, voice biometrics, and AI-powered intent recognition.

Design and implement SystemVerilog RTL and microarchitectures for AI-optimized memory systems; perform synthesis and PPA analysis; document microarchitecture; evaluate tradeoffs and drive implementations from specification through silicon.

Lead design and delivery of reliable, scalable AI/ML platform services. Build production-grade code, reusable APIs/SDKs, observability and resilience tooling, define reliability standards, support on-call incident resolution, and mentor engineers to enable secure, compliant generative AI use cases.

Develop and operate Java 21+ microservices using Spring Boot, REST APIs, event-driven integrations, relational databases, Docker, Kubernetes, and Azure. Modernize legacy monoliths, build automated tests, contribute to CI/CD and infrastructure automation, monitor production systems, and support incident response. Use Claude Code and other AI coding agents throughout development while evaluating generated code for security, reliability, maintainability, performance, and coverage. Own service architecture and evolution while collaborating with product, platform, security, QA, and business teams.

Owns the vision, roadmap, prioritization, and lifecycle of enterprise AI infrastructure supporting model development and inference at scale. Partners with technology, security, finance, audit, risk, and business stakeholders to align roadmaps, budgets, requirements, and risk management. Leads product selection, RFx activities, feature prioritization, and communication with senior leadership while overseeing product performance, inventory, lifecycle, and overall health.

Build and integrate enterprise AI solutions using pre-trained LLMs, RAG architectures, traditional machine learning, AI agents, and deterministic logic. Develop backend APIs, React interfaces, data pipelines, and integrations with ServiceNow, SAP, Salesforce, and Azure. Support testing, deployment, monitoring, documentation, and workflow orchestration using LangChain and n8n while collaborating with AI, data, and platform teams.

Build and integrate enterprise AI solutions using pre-trained LLMs, RAG architectures, traditional machine learning, AI agents, and deterministic logic. Develop backend APIs, React interfaces, data pipelines, and integrations with ServiceNow, SAP, Salesforce, and Azure. Support testing, deployment, monitoring, documentation, and workflow orchestration using LangChain and n8n. Collaborate with AI, data, and platform teams on production-ready solutions while developing applied Generative AI engineering skills.

Lead technical direction for cloud platform engineering: design, build, and operate secure, scalable platforms supporting AI/ML and data initiatives. Drive DevEx, CI/CD, DevSecOps, observability, self-service developer workflows, and AI-assisted development including agent and MCP integrations.

Individual contributor lead role to architect, build, and deploy agentic LLM systems, RAG/hybrid search, and scalable microservices on cloud/Big Data platforms. Owns MLOps/LLMOps, evaluation frameworks, HIPAA-compliant security controls, and integrations with data and internal AI platforms. Partners with data engineering, mentors engineers, prototypes POCs, and drives production reliability and cost KPIs.

Lead product strategy and delivery for Capital Ones AI coding and developer experience tools. Translate frontier AI into governed, developer-loved products that scale AI-augmented workflows, improve code review, embed enterprise standards, and accelerate software delivery through cross-functional leadership and data-driven decisions.

Design, develop, and deploy production-grade full-stack AI applications and agentic AI solutions. Build AI agents, multi-agent workflows, RAG pipelines, LLM applications, conversational interfaces, and scalable microservices using Java or Python and AWS services. Implement prompt engineering, evaluation, observability, guardrails, CI/CD, containerization, APIs, and infrastructure as code. Troubleshoot and optimize production systems in enterprise environments.

Lead workload-driven AI/ML systems architecture across hardware and software. Own workload characterization, analytical performance modeling, KPI definition, and hardware performance analysis to guide SoC, memory, and HW/SW co-design decisions. Evaluate compute, memory, latency, and software bottlenecks; collaborate with CPU, compiler, runtime, and ML framework teams; present recommendations to leadership; and mentor junior engineers. The role requires expertise in processor and memory architecture, edge AI acceleration, performance modeling, and cross-functional technical leadership.

Lead and scale a Data & AI Support Engineering team to resolve complex Spark, streaming, Lakehouse, and Databricks issues. Build AI-enabled support workflows, automations, and runbooks; partner with Engineering/Product to operationalize diagnostics and observability; manage KPIs, escalations, hiring, training, and on-call incident response while acting as a technical escalation leader for enterprise customers.

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.

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

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.

Design and implement production-grade backend services and platform APIs in Go. Build Kubernetes-native components (controllers, operators, CRDs, webhooks), integrate with CI/CD, observability, and security systems, and apply AI/agentic programming to automate platform workflows and improve developer productivity. Troubleshoot distributed systems and participate in architecture reviews.

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.