Senior AI Engineer

Duration: 12 Months

Employment Type: W2

Need Local Candidates as the final round of interview will be in-person.

Not open for H1B or H1B transfer

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.

• Build and operate production agentic AI systems, including deployed, monitored, and debugged agents running at scale.
• Own agentic use cases end-to-end, partnering directly with business stakeholders from problem definition through delivery and operational support.
• Design orchestration patterns for autonomous or semi-autonomous agents using modern agent frameworks and production orchestration layers.
• Develop enterprise capabilities as tools or skills for LLMs, enabling agents to interact with business systems and workflows in a controlled, scalable way.
• Engineer reliable backend services for agentic workloads, with emphasis on Python-based development and integration with Java service layers where required.
• Implement and support production infrastructure for AI workloads, including environments where Kubernetes and model-serving components such as vLLM may be part of the stack.
• Evaluate and communicate system failure modes, including the ability to walk through shipped systems, operational issues, debugging approaches, and mitigation strategies.
• Collaborate with vendor, engineering, and business teams to deliver solutions with limited ramp-up time, consistent with expectations for senior contract engineering talent.
• Maintain a production-first engineering standard, ensuring the role does not over-index on framework familiarity at the expense of real deployment experience.

• 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 Lang Chain and/or Lang Graph.
• 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.

• Experience with Lang Graph 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.