Artificial Intelligence Jobs in Richardson, TX

Support Strategic Portfolio Management by collecting, validating, and analyzing financial, operational, and portfolio data. Perform budgeting, forecasting, variance and scenario analyses. Prepare executive reports, dashboards, and presentations. Partner cross-functionally with Finance, Technology, and Business teams and assist with governance, tracking action items, risks, dependencies, and milestones.

Underwrite and assess multifamily loan packages prior to acquisition, evaluating property, sponsor, and market risks to determine supportable loan sizing. Work with internal and external partners to recommend lending decisions, document negotiated outcomes, and approve issuance. Enhance underwriting skills across diverse multifamily property types.

Participate in a two-day hackathon solving nonprofit data problems and network with Data & AI teams. Potential pathway to a 2027 Data & AI summer internship where you would build end-to-end data, analytics, and ML solutions, design data platforms and pipelines, develop production-ready models and dashboards, ensure data governance and compliance, work on agile teams, and deliver data-driven recommendations.

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.

Lead a team delivering cloud-based, full‑stack enterprise platforms using distributed microservices. Collaborate with product managers, mentor engineers, experiment with new tech (including AI tooling), and build scalable solutions using languages like Java/Go/JavaScript and container orchestration on AWS and other cloud providers.

Lead and scale multiple AI-focused engineering teams to design, build, and deploy production-grade LLMs and generative AI solutions. Drive MLOps, low-latency inference microservices, CT/model lineage, and full-stack AI integrations with web and mobile frontends while partnering with product and data science.

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 Software Engineer responsible for designing, building, and maintaining scalable data pipelines and platform components (cataloging, quality, lineage, governance). Implements data modeling, partners with stakeholders to deliver production-grade code, automates operations, evaluates vendors, and leads Agentic Autonomous Lakehouse and engineering communities to drive platform innovation and operational stability.

Lead Cisco's IP strategy for AI by developing policies and documentation standards for patents, copyright, and trade secrets; run copyright and trade-secret programs; advise on AI-related patent strategies and vendor agreements; and translate legal and regulatory developments into executive-ready guidance.

As a Member of Technical Staff, design and implement high-performance memory solutions for AI, focusing on microarchitecture, performance, power, and reliability.

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 AI/ML-focused information security for enterprise agentic capabilities. Advise projects and CSOC teams on threat mitigation, compliance, automation, monitoring, and incident response. Partner with engineering and stakeholders to secure cloud, generative AI, and customer servicing platforms while driving data-driven decisions and continuous improvements.

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.

The Senior Product Owner leads product development for Mainframe solutions at Broadcom, collaborating with cross-functional teams to deliver features based on customer needs and product roadmaps.

Lead design, development, deployment, and support of scalable GenAI platform components (foundation model training, LLM inference, vector search, guardrails, evaluation, observability). Optimize training and inference performance, contribute to technical vision and roadmap, and partner with cross-functional teams to deliver responsible, production-grade AI systems.

Lead and deliver secure, scalable Java-based microservice solutions; drive adoption and safe use of enterprise AI-assisted engineering tools; produce architecture and high-quality production code; improve CI/CD, data models, and system reliability while mentoring engineers and contributing to engineering communities.

Lead software engineer responsible for designing, developing, and delivering secure, scalable Java/Spring microservices. Drive team adoption of enterprise-authorized AI-assisted engineering practices, produce architecture artifacts, improve code quality via automation and CI/CD, troubleshoot complex issues, and coach engineers on responsible AI and secure coding standards.

Lead design and deliver AI-enabled data platform capabilities (metadata, lineage, data quality, retention). Drive adoption of enterprise-authorized AI-assisted engineering, build agentic production systems, develop secure production code, automate remediation, establish production SRE controls, and mentor teams on responsible AI and validation standards.

Lead hands-on engineer designing and modernizing cloud-native, reliable, and automated software solutions. Drive adoption of enterprise AI-assisted engineering practices, build automation frameworks, support performance and resiliency testing, lead code reviews, and communicate technical strategy and reliability standards across teams.

Lead design and deliver cloud-native microservices and AI/LLM-driven features. Architect scalable, secure systems, drive AI-assisted engineering practices, improve observability/automation, mentor teams, and integrate generative AI into business workflows.

Lead vision, strategy, and roadmap for Open Mainframe Solutions (Zowe and related tools). Advocate customer needs to R&D, prioritize features, unblock cross-functional teams, drive GTM and adoption, measure KPIs, and maintain executive and customer relationships to ensure product success.

Lead a team and portfolio building full-stack, cloud-native microservices and platforms. Collaborate with product managers to deliver scalable AWS solutions, mentor engineers, experiment with new technologies, and ensure regulatory needs are met using languages like JavaScript, Java, TypeScript, Python, Go, and container orchestration (Docker, Kubernetes).

Lead an agile engineering team to design, develop, and operate secure, scalable software incorporating generative AI and LLMs. Drive adoption of enterprise-authorized AI-assisted development practices, evaluate vendor solutions, improve automation and reliability, and mentor engineers on responsible AI, performance, and operational stability.

Own and ship production-grade multi-agent AI systems that automate software delivery pipeline stages. Design, build, and maintain reliable, observable, and cost-effective agent capabilities; implement evaluation and quality mechanisms; lead cross-functional teams; ensure operational stability, clear failure modes, and scalable production code; influence senior stakeholders.