AI Engineer

Info Dinamica Inc

Plano, TX, United States

4 days ago

Role details

Contract type

Permanent contract

Employment type

Full-time (> 32 hours)

Experience level

Expert

Experience required

7 years minimum

Working hours

Regular working hours

Job source

www.dice.com

Tech stack

Java (Programming Language) Artificial Intelligence Software Debugging Java Web Services Python (Programming Language) Software Engineering Systems Architecture Large Language Models Multi-Agent Systems Reliability of Systems Generative AI Backend

+2 more

Kubernetes Virtual Agents

Job description

Primary Skills: Senior AI Engineer, Agentic Systems, Production Agentic AI, Java, Python Role Summary:

The Senior AI Engineer, Agentic Systems will design, build, deploy, and operate production-grade agentic AI systems that support enterprise-scale use cases.
The role emphasizes shipped production experience, system reliability, orchestration, monitoring, debugging, and the ability to expose enterprise capabilities as tools or skills for LLM-powered agents.

Requirements

7+ years of software engineering experience, with demonstrated experience building and shipping production systems.
Hands-on production experience with agentic AI or GenAI applications, including deployment, monitoring, debugging, and operating agents at scale.
Strong Python engineering skills, with the ability to work effectively in production AI and agentic system environments.
Java experience or willingness to work daily within a Java service layer, especially where enterprise systems require integration with existing backend services.
Experience with agent frameworks and orchestration technologies, including LangChain and/or LangGraph.
Familiarity with production infrastructure for AI systems, including Kubernetes-based deployment environments.
Ability to explain shipped system architecture and failure modes, including what was deployed, how it was monitored, where it failed, and how issues were resolved., * Experience with LangGraph as a production orchestration layer.
Experience with vLLM or comparable model-serving infrastructure.
Experience in regulated-industry or financial-services technology environments, especially where enterprise scale and production-path stakes are important.
Experience working with business stakeholders to deliver end-to-end AI use cases, not only platform or prototype work.