Senior AI Engineer, Agentic Systems - W2 Role

We are seeking a Senior AI Engineer, Agentic Systems to design, build, and operate production-grade agentic AI solutions for enterprise-scale use cases. This role requires hands-on experience shipping AI systems into production, operating them reliably, debugging real-world failure modes, and exposing enterprise capabilities as tools or skills for LLM-powered agents. The ideal candidate combines strong Python engineering, practical agent orchestration experience, and the ability to partner with business stakeholders to own use cases from concept through production delivery.

• 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.
• Comfort working in a senior contract delivery model where the expectation is faster delivery and limited ramp-up investment.

Preferred Qualifications

• 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.
• Ability to operate in an onsite or hybrid delivery model, especially in a market such as NYC where the attachment notes a deeper finance-AI contractor pool.