Lead Artificial Intelligence Engineer - Hybrid in Dallas, TX - Direct Hire (NOT Contract)

Are you passionate about building the future of AI? We are seeking a Lead AI Engineer to drive the strategy, architecture, and delivery of Agentic and Generative AI solutions across a fast-growing technology portfolio. In this highly visible leadership role, you will architect scalable AI systems, mentor engineering teams, and partner with executives and product leaders to deliver innovative, production-grade AI experiences.

This is a hybrid role requiring 2-3 days onsite weekly (Tuesdays and Wednesdays onsite).

What You'll Do:

• Lead the end-to-end architecture and technical roadmap for AI products and platforms, including LLM strategy, RAG, multi-agent systems, and workflow orchestration.
• Design scalable AI solutions leveraging foundation models, multimodal AI, retrieval systems, semantic search, and knowledge graphs.
• Build reusable AI capabilities, including shared RAG frameworks, SDKs, prompt standards, and agentic backend services.
• Establish best practices for AI observability, governance, evaluation, logging, and responsible AI development.
• Mentor and guide AI Engineers, ML Engineers, and Data Scientists, conducting architecture and code reviews while fostering engineering excellence.
• Partner with product, design, and business leaders to identify high-impact AI opportunities, define roadmaps, and deliver measurable outcomes.
• Drive performance, reliability, and cost optimization through model routing, caching, infrastructure scaling, and build-vs-buy decisions.
• Ensure AI solutions meet security, privacy, compliance, and governance standards.

• 8+ years of experience in Software Engineering, ML Engineering, or Data Science, including:
• 3+ years of hands-on experience with Applied AI/LLMs
• 2+ years in a technical leadership or lead engineering role
• Deep expertise in Python and TypeScript/JavaScript in production environments.
• Proven experience architecting and deploying production-scale AI systems using:
• LLMs, prompt engineering, function/tool calling, and multi-agent workflows
• RAG architectures, vector databases, and retrieval optimization
• AI observability, monitoring, and evaluation frameworks
• Strong experience designing and operating cloud-native systems in AWS, GCP, or Azure.
• Hands-on experience with Docker, Kubernetes, CI/CD pipelines, and distributed systems.
• Experience using AI coding assistants such as Cursor, Windsurf, or Codex.
• Demonstrated success leading technical initiatives, mentoring engineers, and balancing speed, quality, and cost tradeoffs.
• Excellent communication skills with the ability to explain complex AI concepts to both executives and technical stakeholders.

Preferences:

• Experience with multimodal AI, real-time agents, voice AI, or streaming interactions.
• Familiarity with AI evaluation frameworks such as OpenAI Evals or LangSmith.
• Experience with data platforms, event streaming (e.g., Kafka), feature stores, or data lakes/warehouses.
• Knowledge of browser automation tools such as Playwright.
• Experience designing AI systems in regulated or privacy-sensitive environments.
• Background building organizational AI strategies, engineering standards, and AI capability roadmaps.