Artificial Intelligence Jobs in Irving, TX

Build and maintain Python-based services that integrate Generative AI into enterprise data and ML platforms. Design, implement, and scale cloud-hosted solutions, mentor junior developers, ensure governance and security compliance, and evaluate emerging AI technologies to improve developer productivity and business value.

Lead strategy and execution for Generative AI initiatives: build and mentor a Gen AI team, architect LLM-based solutions, integrate vector/graph databases and speech-to-text, apply advanced retrieval techniques, collaborate cross-functionally, and drive adoption and ROI through continual research and stakeholder communication.

Design, build, and deploy production Generative AI applications: orchestrate multi-agent workflows, implement advanced RAG with vector DBs, integrate and fine-tune LLMs, develop scalable FastAPI/async backends, containerize with Docker, and optimize GenAI systems for latency, cost, and reliability.

Build and scale production Generative AI applications: design multi-agent systems and LangGraph workflows, implement advanced RAG with LlamaIndex and vector DBs, integrate and fine-tune various LLMs, and deploy containerized FastAPI services with monitoring, CI/CD, and performance/cost optimizations.

Design, build, and deploy production-grade generative AI applications: orchestrate multi-agent systems, implement advanced RAG with vector DBs, fine-tune and host LLMs, optimize GenAI workflows, and deliver scalable FastAPI-based backend services with Docker and monitoring.

Design, build, and scale production Generative AI systems: multi-agent orchestration, advanced RAG with vector databases, LLM integration and fine-tuning, scalable FastAPI backends, Dockerized deployment, monitoring, and GenAI optimization for latency, cost, and reliability.

Design, build, and productionize generative AI solutions: advanced Python development, LLM prompt engineering and fine-tuning, agent frameworks, RAG pipelines with vector DBs, API and cloud deployment, MLOps/LLMOps, model evaluation and Responsible AI practices, and translate business needs into scalable AI applications.

Design, build, and scale production Generative AI applications: multi-agent systems, advanced RAG pipelines, LLM integration and fine-tuning, scalable FastAPI backends, vector search, monitoring, and Dockerized deployments.

Build and scale production-ready generative AI applications: design multi-agent workflows, implement advanced RAG with vector DBs, integrate/swap LLMs, fine-tune models, and deploy scalable FastAPI/Docker services with monitoring and optimization for latency, cost, and reliability.

Design, build, and scale production-ready generative AI systems: multi-agent workflows, advanced RAG pipelines, LLM integration and fine-tuning, scalable FastAPI backends, Dockerized deployments, monitoring, and GenAI optimization for latency, cost, and reliability.

Design, build, and scale production Generative AI applications including multi-agent systems, advanced RAG pipelines, and local/frontier LLM deployments. Implement high-performance backends with FastAPI and async Python, fine-tune and quantize models, integrate vector databases and retrieval systems, and deploy/monitor containerized AI services for reliability, latency, and cost optimization.

Design, build, and deploy production-grade generative AI systems: multi-agent orchestration, advanced RAG with vector databases, integrate/swap LLMs, fine-tune models, optimize prompts and latency, and containerize services with monitoring and CI/CD.

Design, build, and deploy production-ready generative AI applications: architect multi-agent workflows, implement advanced RAG with vector DBs, integrate/swap LLMs, fine-tune models, and deliver scalable FastAPI-based services with monitoring, CI/CD, and containerized deployment.

Build, optimize, and scale production Generative AI applications. Design multi-agent systems and LangGraph workflows, implement advanced RAG with LlamaIndex and vector DBs, fine-tune and deploy LLMs, and create high-performance FastAPI async backends. Improve latency, cost, and reliability, containerize services with Docker, and manage monitoring, API security, and CI/CD for ML deployments.

Design, build, and deploy production generative AI applications: orchestrate multi-agent workflows, implement advanced RAG with vector databases, integrate/benchmark LLMs, fine-tune models, and deploy scalable FastAPI-based services with monitoring, CI/CD, and Dockerized production pipelines.

Build and scale production Generative AI applications: design multi-agent systems and LangGraph workflows, implement advanced RAG with vector DBs, integrate and fine-tune LLMs, deploy FastAPI async services in Docker, and optimize GenAI performance, cost, and monitoring.

Lead AI FinOps for enterprise AI platforms by managing cloud AI spend, optimizing token/GPU/inference costs, building forecasting and chargeback models, defining governance and financial guardrails, partnering with cross-functional teams, and delivering executive reporting to drive cost-effective, responsible AI adoption.

The AI/Full Stack Tech Lead develops SBL solutions, automates analysis of legal documents, maintains data infrastructure, and deploys AI models on OpenShift, bridging engineering and business partners.

Lead design and development of scalable full-stack applications using Java, Angular, microservices, and event-driven architectures. Integrate and deploy agentic AI/GenAI/LLM solutions for M&A use cases, architect resilient systems, implement CI/CD and cloud/container platforms, promote engineering best practices, and mentor teams across the SDLC while partnering with Architecture, Security, Risk, and Business stakeholders.

Lead technical vision and architecture for enterprise ML platforms and MLOps. Design training, feature, serving, and observability systems; establish ML lifecycle and governance; solve complex cross-team engineering problems; mentor senior engineers; evaluate emerging AI technologies; drive cost optimization, compliance, and organizational alignment.

Design, build, and deploy ML models and end-to-end pipelines; implement MLOps practices; deploy containers to cloud production; monitor model performance and drift; collaborate with engineers and product teams; optimize latency and throughput; write production-quality code and mentor junior engineers.

The Director will lead the strategy and implementation of a developer platform with an emphasis on AI-driven management of test environments, overseeing operational integrity, compliance, and driving strategic initiatives to enhance developer experience and efficiency.

Lead and deliver a portfolio of Data, AI, and ML products for retail pharmacy growth and operations. Define AI strategy toward agentic automation, architect cloud-native ML solutions, enforce security and HIPAA-compliant data governance, scale Claude Skills and MCP integrations, run engineering standards and CI/CD/DataOps, and grow high-performing engineering teams while partnering with senior stakeholders to drive measurable business outcomes.

Lead applications systems analysis and programming activities, integrating functions, resolving complex problems, providing expertise in applications programming, and mentoring mid-level developers.

Lead AI Quality Engineering initiatives for enterprise AI, establishing AI-native testing, validation, runtime quality controls, observability, and automation. Coordinate cross-functional validation, release readiness, and production reliability for LLM-enabled workflows and AI platforms. Mentor teams and drive adoption of AI-assisted testing, telemetry analysis, and continuous improvement across healthcare-related AI solutions.