AI Engineer
Info Dinamica Inc
Plano, TX, United States
2 months ago
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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
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.
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