AI Agent Engineer

Houston, United States | Posted on 01/20/2026

Lead AI Engineer (AI Agents, Automation & Multimodal Systems)

Location: Remote / Hybrid (if local)

Employment Type: Full-Time

Reports To: Head of AI / CTO / Engineering Leadership

We’re seeking a Lead AI Engineer to own the architecture, development, and deployment of next-generation AI agent systems across language, voice, vision, and automation
. This role is ideal for someone who thrives at the intersection of engineering rigor and applied AI
, and who can turn emerging AI capabilities into reliable, production-grade systems.

You’ll lead the design of AI-powered workflows that combine LLMs, APIs, multimodal models, automation platforms, and conversational interfaces to solve real business problems. This role is both hands-on and strategic —you’ll build systems yourself while setting technical direction, standards, and best practices for the team.

• Design and implement scalable AI agent architectures using LLMs, tool calling, retrieval, memory, and orchestration frameworks.
• Own multi-step, multi-agent workflows for complex tasks and decision-making.
• Make architectural decisions around when to use LLMs vs. deterministic systems.

• Lead prompt strategy across models (GPT-4, Claude, Gemini, open-source models).
• Develop reusable prompt patterns, chains, and templates.
• Ensure prompts are optimized for reliability, safety, latency, and cost.

• Architect and build end-to-end AI workflows using tools like n8n, Lang Graph, Auto Gen, or custom orchestration layers.
• Integrate AI agents with backend systems (CRMs, databases, internal tools, third-party APIs).
• Implement robust error handling, retries, observability, and logging.

• Lead design of production-grade voice agents and conversational systems.
• Define best practices for spoken dialogue, intent handling, fallback strategies, and escalation paths.
• Ensure compliance, safety, and reliability in voice and conversational use cases.
• Optimize for low latency, natural language flow, and user trust.
• Design and deploy multimodal workflows combining vision, language, and automation.
• Apply vision models for use cases such as object detection, quality control, or document understanding.
• Integrate vision outputs into downstream agent decision-making.

• Define evaluation frameworks for AI outputs (accuracy, hallucination risk, safety, bias).
• Build automated testing and QA pipelines for agent systems.
• Monitor performance in production and continuously improve systems.
  
  Set technical standards and best practices for AI development.
• Mentor interns and junior engineers on AI workflows and engineering rigor.
• Collaborate closely with Product, Design, and Enablement teams to translate business needs into AI solutions.

• 6+ years of experience in software engineering, with deep focus on applied AI systems.
• Strong experience working with LLMs and AI APIs (OpenAI, Anthropic, Google, or similar).
• Proven experience building production AI systems
  , not just prototypes.
• Experience with APIs, microservices, and backend integrations.
  
  Strong understanding of system reliability, scalability, and observability.
• Excellent communication skills and ability to explain complex systems clearly.

• Experience with agent frameworks (Lang Chain, Lang Graph, Auto Gen, CrewAI).
• Experience building voice agents or conversational AI systems.
• Experience with computer vision or multi modal models.
• Familiarity with no-code/low-code automation tools (n8n, Zapier).
• Experience working in small, fast-moving teams or startups.

• Ownership of cutting-edge AI systems from concept to production.
• Opportunity to define and scale an AI agent platform.
• Direct impact on product direction and business outcomes.
• Leadership growth through mentoring and technical decision-making.
• Competitive compensation and growth opportunities (details shared during process)

This role is not about experimentation alone—it’s about turning AI into dependable infrastructure
. You’ll help shape how AI is responsibly and effectively used across the organization, setting the foundation for future innovation.