Artificial Intelligence Engineer

Harnham; Houston (TX); On-site; USD 150,000 - 230,000; Full time

Job summary

Harnham is seeking a Senior AI Engineer in Houston, TX to build production-grade AI agents and agentic systems. You will design architectures, orchestrate multi-step workflows, and integrate LLMs with enterprise APIs and data sources.

You will lead backend and frontend efforts, develop data pipelines, and ensure security, reliability, and performance in production environments. On-site role with strong emphasis on end-to-end delivery.

Qualifications

• Bachelor’s degree in computer science, software engineering, or related field.
• 8+ years of professional software engineering experience building production applications.
• Strong backend/software engineering experience with Python and RESTful APIs.
• Hands-on experience building LLM-powered applications, AI agents, or agentic workflows.

Responsibilities

• Design, build, and deploy production-grade AI agents capable of multi-step workflows.
• Develop agentic systems that reason over data, invoke tools, and interact with APIs.
• Build secure mechanisms for agents to interact with enterprise systems and tools.
• Implement guardrails, human-in-the-loop workflows, and observability for production deployment.

Skills

Python

LLM apps

Agent orchestration

Memory management

Context engineering

API integration

Distributed systems

Frontend: React/Next.js

Data pipelines

Databricks

GCP

CI/CD

AI coding tools

Education

Bachelor’s in CS

Tools

LangGraph

LangChain

LlamaIndex

AutoGen

MCP

Job description

We are looking for an AI Engineer that has experience in building support agents coming from a Fullstack Software Engineering background.

Location: Houston, TX — On-site

As a Senior AI Engineer, you will:

AI Agent & Agentic Systems Development

• Design, build, and deploy production-grade AI agents capable of executing complex, multi-step workflows.
• Develop agentic systems that can reason over structured and unstructured data, select and invoke tools, interact with APIs and enterprise systems, and take actions on behalf of users.
• Build intelligent support agents that can understand user intent, retrieve relevant information, maintain context, resolve requests, and escalated to human operators when required.
• Design agent architectures including orchestration, planning, tool routing, state management, memory, and context management.
• Develop multi-step and multi-agent workflows that combine LLMs, business logic, APIs, RAG, structured data, and external tools.
• Build secure mechanisms for agents to interact with enterprise applications, databases, internal systems, and third-party services.
• Implement guardrails, fallback mechanisms, permissions, and human-in-the-loop workflows for reliable production deployment.
• Develop RAG systems that allow agents to retrieve and reason over enterprise knowledge and operational data.
• Build retrieval, context, and memory pipelines to support agent performance and personalization.
• Design prompting and context-engineering strategies for complex agentic workflows.
• Integrate and orchestrate LLMs from providers such as OpenAI, Anthropic, or Google.
• Evaluate models, agent architectures, retrieval strategies, and tool-use approaches based on accuracy, latency, cost, reliability, and task completion.
• Develop automated evaluation frameworks and monitoring to measure agent quality and identify regressions.
• Stay current with emerging agent frameworks, protocols, and architectures including LangGraph, LangChain, LlamaIndex, MCP, and similar technologies.

Software Engineering & Full-Stack Development

• Build the production software infrastructure required to support AI agents and intelligent applications.
• Develop scalable backend services, APIs, orchestration layers, and integrations using Python and frameworks such as FastAPI, Flask, or Django.
• Build user-facing applications that allow users to interact with AI agents, workflows, and intelligent systems.
• Develop frontend experiences using React, Angular, Next.js, or similar modern frameworks.
• Design and implement RESTful APIs and services that connect AI agents to enterprise applications and data sources.
• Own features end-to-end across backend services, AI orchestration, data integrations, and frontend experiences.
• Apply strong software engineering principles around architecture, modularity, testing, security, and maintainability.

Data & Agent Infrastructure

• Build and maintain data ingestion and transformation pipelines that support AI applications and agentic workflows.
• Develop data pipelines supporting RAG, agent memory, context, retrieval, and personalization.
• Integrate AI applications with data platforms including Databricks and Lakehouse architectures.
• Connect agents to databases, APIs, ML model endpoints, enterprise applications, and external tools.
• Ensure data quality, security, reliability, and performance across AI and application pipelines.
• Deploy and operate AI agents and supporting applications in production cloud environments.
• Implement CI/CD, automated testing, observability, logging, and monitoring across AI and software systems.
• Monitor agent behavior, latency, cost, reliability, and task completion in production.
• Build mechanisms to identify, diagnose, and recover from agent and system failures.
• Contribute to infrastructure-as-code and cloud-native deployment practices.
• Ensure AI systems meet enterprise requirements for security, scalability, reliability, and governance.

AI-Assisted Software Engineering

• Use AI coding tools such as Claude Code, Codex, and Augment Code as part of the day-to-day development workflow.
• Apply AI-assisted development to accelerate delivery while maintaining engineering standards around architecture, testing, security, and code quality.
• Experiment with emerging AI developer tools and agent frameworks and help establish best practices across the engineering team.

What We Need From You

• Bachelor's degree in Computer Science, Software Engineering, or a related technical field.
• 8+ years of professional software engineering experience building production applications.
• Strong backend/software engineering experience with Python and RESTful APIs.
• Hands-on experience building LLM-powered applications, AI agents, or agentic workflows.
• Experience designing systems involving LLMs, RAG, tool calling, agent orchestration, memory, state management, or context engineering.
• Experience integrating AI systems with APIs, databases, enterprise applications, and external tools.
• Strong understanding of software architecture, distributed systems, APIs, testing, and production engineering.
• Experience with React, Angular, Next.js, or another modern frontend framework.
• Experience building data-intensive applications and/or data pipelines.
• Experience with Databricks, Lakehouse architectures, or similar modern data platforms preferred.
• Experience with agent frameworks such as LangGraph, LangChain, LlamaIndex, AutoGen, or similar preferred.
• Experience with MCP or other approaches to connecting AI agents with external tools and systems preferred.
• Experience with cloud-native application development, preferably GCP.
• Experience with CI/CD, infrastructure-as-code, observability, and production deployments.
• Experience actively using AI coding tools such as Claude Code, Codex, or Augment Code preferred.

What You’ll Bring

• Agent Engineering: You understand how to build AI systems that can reason, retrieve information, use tools, maintain state, and execute multi-step workflows — not simply applications that call an LLM.
• Software Engineering: You have strong engineering fundamentals and can build the backend services, APIs, integrations, and frontend experiences required to turn AI capabilities into production products.
• Production Mindset: You understand that reliable AI requires evaluation, observability, guardrails, testing, security, and continuous improvement.
• Systems Thinking: You are comfortable working across AI, software, data, and cloud infrastructure to solve complex problems end-to-end.
• Hands-On Approach: You enjoy writing code and building systems rather than operating purely at the architecture or strategy level.
• Curiosity: You actively experiment with new models, agent architectures, frameworks, tools, and approaches as the AI ecosystem evolves.
• Ownership: You take responsibility for taking an AI capability from concept through development, deployment, monitoring, and iteration.