Artificial Intelligence Jobs in Plano, TX

Build infrastructure automation and AI-driven operations for a global multi-region cloud platform. Design end-to-end provisioning pipelines, integrate AI agents for incident intelligence and change-risk scoring, and collaborate with SRE/DevOps to translate manual workflows into secure, compliant automation across sovereign regions.

Owner of quality for an LLM-based multi-agent claims adjudication pipeline. Build evaluation frameworks, automated LLM-grading workflows, function-call and reasoning-trace auditing using Vertex AI. Integrate CI/CD regression, Auto-SxS comparisons, and large-scale mocking to ensure compliance and accurate adjudications for DOL/DOI audits.

Lead design and implementation of agentic AI systems for claims adjudication using Gemini and Vertex AI. Build production-grade Python agents, implement RAG, grounding, stateful orchestration with human review, input/output guardrails, and Google Cloud DLP integration. Drive backend patterns, optimize model behaviors, and supervise fine-tuning to ensure reliable, auditable outputs in healthcare claims workflows.

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.

Lead design and delivery of scalable, production-grade agentic AI and automation systems integrated with enterprise collaboration tools (Slack, Outlook, Zoom, SharePoint, Copilot). Drive architecture-to-execution, CI/CD, infrastructure-as-code, monitoring, and cross-team collaboration to scale AI-driven workflows and improve associate productivity.

Design, build, and deploy production-ready AI systems and agents using Python and LLMs. Implement RAG pipelines, vector embeddings, and semantic search. Integrate AI features via REST APIs and microservices, apply prompt engineering, monitor performance, and collaborate with cross-functional teams to deliver reliable, scalable AI-powered applications.

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 design, deployment, and maintenance of large-scale Generative AI solutions (RAG and Agentic AI). Architect secure document processing pipelines, integrate responsible AI practices (toxicity/bias screening), implement data security and compliance, monitor AI infrastructure, and collaborate with cross-functional governance and legal teams.

Design, build, and scale AI-powered operational platforms integrating LLMs, monitoring, CI/CD, ticketing, and cloud services. Develop APIs, data pipelines, front-end apps (TypeScript/React/Next.js), backend systems, evaluation/observability tools, and reusable frameworks. Collaborate across SRE, Product, and Infrastructure to deliver production-grade, automated AI workflows and improve system reliability and performance.

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.

Design, develop, test, and support scalable AI-enabled applications and services. Implement LLM, Generative AI, and RAG capabilities, build RESTful APIs and microservices, automate CI/CD and testing, perform code reviews, troubleshoot production issues, and collaborate with product and architecture teams in Agile delivery.

Lead development and delivery of Fannie Mae's enterprise AI platform capabilities, balancing innovation with security, governance, and cost controls. Own product vision, roadmaps, and core services (models, tooling, agent runtimes), partner with engineering, security, finance, legal, and business teams, onboard priority use cases, and drive adoption through strong stakeholder engagement and developer experience.

Develop and deploy end-to-end ML solutions for smart manufacturing: build data pipelines, feature engineering, CV and LLM-based models (RAG/agentic workflows), prototype and productionize models, implement monitoring/retraining, and collaborate with cross-functional teams using modern development and CI/CD practices.