AI Engineer, Agentic Systems

Appiness Inc.

New York, NY, United States

9 days ago

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Role details

Contract type

Temporary 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 Cloud Computing Software Debugging Java Web Services Python (Programming Language) Software Deployment Software Engineering Workflow Management Systems AI Infrastructure Large Language Models Multi-Agent Systems

+3 more

Generative AI Kubernetes Virtual Agents

Requirements

We are looking for a Senior AI Engineer, Agentic Systems who has hands-on experience building, deploying, monitoring, and operating production-grade Agentic AI systems., * 7+ years of software engineering experience

Strong Python development skills
Proven experience shipping Agentic AI / GenAI applications to production
Hands-on experience with AI agents / LLM-powered systems
Experience with LangChain and/or LangGraph
Production deployment, monitoring, troubleshooting, and debugging experience
Strong understanding of agent orchestration and workflow design
Experience with Kubernetes / K8s
Java experience or ability to work with Java service layers
Experience exposing enterprise capabilities as tools/skills for LLM agents
Ability to explain real-world production failure modes, monitoring, debugging, and mitigation

Preferred Skills

LangGraph production experience
vLLM or comparable model-serving infrastructure
Financial Services / Banking / FinTech experience
Enterprise-scale AI systems
Cloud and AI infrastructure
Experience partnering directly with business stakeholders
End-to-end ownership from use case definition * development * production * support, This is an excellent opportunity for an experienced AI engineer who enjoys fast-paced production delivery and working on enterprise-scale Agentic AI solutions.

Benefits & conditions

An Agentic AI system they personally shipped to production How the system was deployed and monitored Real production failures and how they debugged them Agent orchestration and tool/skill integration How they worked with business stakeholders How they ensured reliability and scalability