Artificial Intelligence Jobs in Richardson, TX

The Chief Compliance Officer will drive operational excellence and oversee regulatory compliance while managing investment operations and financial oversight.

Supports Account Executives and Account Managers on complex opportunities by leading technical discovery, designing solutions across hardware, software, connectivity, and services, and delivering demos, presentations, proposals, POCs, integrations, and technical validations. The role translates customer problems into business-valued technology solutions, coordinates internal teams, builds stakeholder consensus, enables sellers, and applies AI tools to scale pre-sales activities.

Develop and support enterprise Generative AI and Agentic AI applications using Python, LLMs, RAG, AI agents, prompt engineering, APIs, and cloud services. Build chatbots, copilots, intelligent automation, data pipelines, and scalable AI components. Support model evaluation, MLOps/LLMOps, deployment, monitoring, troubleshooting, documentation, and Agile collaboration. The role requires strong learning ability, software development fundamentals, and collaboration with technical and business teams.

Develop and support enterprise Generative AI and Agentic AI applications using Python, LLMs, RAG, prompt engineering, AI agents, cloud services, APIs, and databases. Build chatbots, copilots, automation workflows, and scalable AI components; support MLOps, deployment, testing, monitoring, and system integration. Collaborate with technical and business teams in Agile environments while documenting solutions and improving application reliability.

Leads software engineering teams developing and modernizing scalable, cloud-native systems for claims adjudication and PBM platforms. Responsibilities include people leadership, hiring, delivery execution, architecture guidance, Agile management, cloud cost optimization, production reliability, vendor coordination, and responsible AI tool adoption. The role drives modernization from legacy IBMi/AS400 and RxClaim systems while ensuring security, privacy, regulatory compliance, and effective executive communication.

Supports the Office of the CIO by analyzing investment strategies, portfolio risk, credit data, asset allocation, manager exposures, peer disclosures, and market data. Builds dashboards, monitoring tools, and reports for senior decision-makers; automates risk reporting, exposure aggregation, and ALM scenario workflows; and applies software engineering practices to durable internal tools. Uses Python, SQL, BI platforms, cloud data technologies, and AI tools to improve investment analysis and operational efficiency.

Leads enterprise architecture for data and AI modernization across business domains. Drives metadata standardization, semantic data modeling, governance, analytics and AI enablement. Partners with executive stakeholders, product, engineering, data and AI teams; contributes hands-on to architecture, MLOps, AI model feedback and AI-generated code reviews.

Leads product marketing for RealPage’s Agentic AI solutions, driving go-to-market strategy, positioning, product launches, customer adoption, competitive intelligence, sales enablement, and marketing content. Partners cross-functionally with product, sales, account management, and demand generation teams while representing the customer voice. Develops messaging, buyer journeys, launch KPIs, and strategic recommendations for AI, data, and analytics products serving the multifamily real estate industry.

Engineer and operate AI monitoring, guardrail, observability, and governance applications in an on-premises OpenShift environment. Responsibilities include building APIs and integrations, deploying containerized services, managing model evaluation workflows, developing Grafana dashboards and alerting, troubleshooting production systems, optimizing scalability and performance, and improving controls for agentic AI applications. The role also leads cross-functional initiatives, mentors engineers, and supports operational planning in a regulated enterprise.

Lead production-focused AI and machine learning development across classification, prediction, generative AI, LLM, and agentic use cases. Own modeling, evaluation, deployment, monitoring, and lifecycle improvement for customer-facing capabilities. Partner with Product, Engineering, Data Engineering, and MLOps to assess feasibility, prioritize opportunities, establish evaluation standards, and translate complex automotive data into reliable, scalable AI products. The role also provides technical leadership and product judgment without direct people-management responsibility.

Build, integrate, deploy, and operationalize secure AI solutions across enterprise platforms. Responsibilities include prompt engineering, orchestration, RAG implementation, enterprise data integration, monitoring, performance tuning, documentation, and governance compliance. The role partners with IT, Security, Privacy, Data, and business teams to deliver production-ready AI capabilities using technologies such as OpenAI, Anthropic, Microsoft 365, Salesforce, and ServiceNow.

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.