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Metro location and mid-senior level, but niche LLM deployment reduces generic applicant volume.
Requires LLM deployment experience, finance domain vocabulary and ERP integration, limiting cross-industry transferability.
Explicit 6–10 years, required ML production experience, LLM expertise, Azure and ERP integration imply strict filters.
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Own technical success and outcome for a portfolio of strategic finance-related accounts, delivering AI systems reducing days sales outstanding and deductions.
Lead deployment team, set KPI contracts (accuracy, processing rate, latency, cost), manage solution design, architecture, and production rollout.
Act as primary technical liaison for customers, handle incident management, root cause analysis, deployment governance, and convert learnings into reusable assets.
6 to 10 years building and operating production systems, with at least 2 years in customer-facing delivery roles.
Experience in production AI systems including strong Python skills and depth in LLM, agentic architectures, retrieval, evaluation, guardrails, and observability.
Cloud deployment experience at scale; Azure preferred, including containers and orchestration.
Sufficient finance domain knowledge (DSO, deductions, remittance advice, cash application) to engage without translation.
Experienced in owning end-to-end delivery and technical architecture of complex AI systems with measurable business impact in finance.
Proven leadership in managing and upskilling deployment engineers, and balancing customer expectations with scope control.
Comfortable in direct customer engagement including advisory on governance, human oversight, and responsible AI in finance technology environments.