AI Engineer – Agent Development

Location: Richardson, TX – Hybrid (Tuesday–Thursday onsite)

Duration: Through February 28, 2027, with possible extension

Schedule: Monday–Friday, 8:00 AM–5:00 PM

Employment: Contract, with potential FTE conversion

Candidates must be authorized to work in the U.S. without current or future sponsorship. No C2C or third-party applicants.

About the Role

We are seeking an experienced AI Engineer to design, build, and deploy production-grade AI agents using Google Agent Development Kit (ADK) and modern agent frameworks. You will build multi-agent systems that integrate with enterprise data platforms, APIs, and business applications while taking solutions from prototype to reliable production.

Responsibilities

• Design and develop AI agents and multi-agent workflows using Google ADK or comparable frameworks.
• Implement agent orchestration including tool use, function calling, sub-agent delegation, and state/session management.
• Integrate AI agents with enterprise data sources such as Snowflake, Salesforce, and internal APIs.
• Deploy and scale AI solutions on Google Cloud / Vertex AI.
• Evaluate and optimize agent performance, latency, reliability, and cost.
• Build testing, observability, guardrails, and safety controls for AI agents.
• Partner with data engineering and platform teams to ensure agents have access to reliable, governed data.
• Deliver AI capabilities that reduce manual effort and provide measurable business value.

Requirements

• 8+ years of software engineering experience, including recent hands-on experience in applied AI/agent development.
• 3+ years of AI engineering experience preferred within the overall software engineering background.
• Strong Python development skills.
• Hands-on experience building AI agents using Google ADK or comparable frameworks such as LangGraph, AutoGen, or CrewAI.
• Experience with LLM APIs such as Gemini, Claude, OpenAI, or similar.
• Strong understanding of agent architecture, including planning, tool orchestration, memory/state, and multi-agent coordination.
• Experience building and integrating REST APIs and backend services.
• Experience deploying applications on Google Cloud Platform, including familiarity with Vertex AI, Cloud Run, IAM, and Secrets Manager.
• Experience shipping AI features to production, beyond proof-of-concept or prototype development.
• Strong communication skills and ability to work independently in a fast-changing environment.

Preferred Qualifications

• Experience with Model Context Protocol (MCP) or similar agent-to-tool integration standards.
• Strong SQL skills, preferably with Snowflake.
• Experience with Snowflake Cortex / Cortex Analyst.
• Familiarity with Salesforce data models and object relationships.
• Experience with LLM/agent evaluation frameworks.
• Experience integrating AI agents with React/Next.js, Node.js, or NestJS applications.
• TypeScript/JavaScript experience.

What Success Looks Like

• Reliable and observable AI agents operating successfully in production.
• Clean integration between AI agents and enterprise data and systems.
• Reduced manual effort through effective, well-designed agent automation.