Artificial Intelligence Jobs in Frisco, TX

As an Automation & AI Technology Experience Intern, you will analyze technology processes, implement automation solutions, and enhance service delivery through self-service initiatives.

The AI Product Manager is responsible for strategy and execution of AI solutions, managing product roadmaps, collaborating with teams, and improving AI performance through metrics and user feedback.

Design and deliver production agent runtimes and multi-agent frameworks (policy execution, planners, state/memory, tool-routing). Implement reasoning loops (ReAct, CoT, ToT), safety controls, deterministic tool‑chaining, observability/evaluation pipelines, and scale platforms on Kubernetes. Mentor engineers, define SLOs and telemetry, integrate RAG/graph memory, and collaborate with product, data, and backend teams while ensuring compliance and PII‑minimization.

Lead AI capability strategy and implementation for Salesforce (SFDC). Translate business needs into AI-enabled requirements, design and deliver solutions, run POCs, establish governance, assess vendors, ensure secure scalable integrations, and align cross-functional stakeholders to drive AI-enabled sales productivity and user experience.

Lead design and delivery of scalable, secure AI/GenAI applications: gather requirements, architect LLM-driven solutions (RAG, vector DBs, prompt engineering), build resilient microservices and pipelines, ensure Responsible AI and healthcare compliance, coordinate testing and production readiness, and lead cross-functional teams and vendors to achieve business value.

The Lead AI Enterprise Data Engineer will architect scalable data platforms, develop AI solutions, and create real-time dashboards, while overseeing AI model deployment and integration with existing systems.

Lead design and delivery of enterprise-scale AI/GenAI platforms and Databricks architectures across major cloud providers. Drive technical strategy, client discovery, reference architectures (data pipelines, feature stores, model training, vector DBs, real-time inference), mentor teams, define responsible-AI and security best practices, and represent the firm externally.

As a Manager, Applied Research, you will lead a team of scientists to drive innovative AI research into production solutions, mentor team members, and collaborate with product leaders to define AI strategies.

Design, develop, test, and maintain cloud-native, high-volume transactional applications using .NET, C#, React/Angular and SQL Server. Build secure, high-performance RESTful APIs, optimize databases, implement CI/CD and DevSecOps practices, mentor junior developers, and participate in architecture and technical design reviews.

The Senior Software Engineer - AI will design and implement AI solutions for legal workflows, ensure robust system architecture, collaborate with clients, and evaluate new AI techniques. Responsibilities include developing workflows and improving system observability.

Design, build, and maintain batch and streaming data pipelines on GCP (BigQuery, dbt, Airflow, Pub/Sub). Productionize ML workflows (training, inference, CI/CD) using Cloud Run, GKE, and Vertex AI. Support real-time inference, RAG/LLM assistants, graph-based recommendations, and model monitoring. Collaborate with cross-functional teams to deliver scalable, reliable data products and observability for downstream ML and analytics.

Own AI cost visibility and optimization: design showback/chargeback and cost-optimization models for GenAI/LLMs, manage enterprise license and seat programs, monitor/analyze spend, flag anomalies, feed dashboards and reporting, and partner with engineering and finance to reclaim unused spend and ensure accurate cost attribution.

Lead architecture and delivery of production AI agent systems: design agent infrastructure, observability, evaluation, progressive rollout, and reliability. Provide hands-on technical leadership, mentor senior engineers, drive RFCs/ADRs, and partner with product to sequence strategic platform work.

Design and build backend services (Python/FastAPI/Postgres/AWS) for agentic AI features, integrate LLM providers, productionize orchestration, telemetry and observability, collaborate with Labs and product teams, and mentor engineers to deliver scalable solutions for legal professionals.

Design, build, and deploy production-grade LLM agents, RAG systems, voice/image AI, and automation workflows. Implement TTS/STT and generative image models, develop full-stack APIs/microservices, optimize performance, and ensure security/compliance while collaborating with product and operations teams.

Lead design and implementation of enterprise-scale AI systems including RAG architectures and autonomous agents; lead full-stack development across AI orchestration, APIs, microservices and web UIs; set technical strategy, mentor engineers, champion MLOps/LLMOps, ensure security/ethical compliance, and collaborate cross-functionally to deliver scalable AI products.

As a Lead Software Engineer, you will design AI-driven backend systems for accounting, integrating LLMs, ensuring performance and security, and collaborating with teams on AI solutions.