What are the responsibilities and job description for the Vice President, AI Engineer - Full Time - Hybrid - 5566 position at Benchmark IT - Technology Talent?
Vice President, AI Engineer - Full Time - Hybrid
About the Role
Our client, a leading NYC fintech firm, is hiring a VP, AI Engineer to lead the design and delivery of production-grade AI systems that drive measurable business outcomes. This is a hands-on technical leadership role for a seasoned engineer who has shipped complex AI systems end-to-end and can translate ambiguous business needs into well-scoped solutions. You will own key workstreams, set technical direction, mentor engineers, and partner directly with business stakeholders to ensure AI capabilities meet production standards for reliability, scalability, and measurability.
What You'll Do
- Lead the architecture and delivery of production AI systems; document intelligence (IDP), knowledge systems and agentic orchestration.
- Own AI projects end-to-end: scoping, stakeholder alignment, design, deployment, monitoring and continuous improvement.
- Drive technical and architectural decisions (API design, system decomposition, evaluation strategy, infrastructure) that raise the quality bar across the platform.
- Build robust evaluation frameworks - statistically sound metrics, benchmark datasets and strict versioning for reproducibility and continuous improvement.
- Partner with Operations, Legal, Product and Business teams to identify opportunities and communicate tradeoffs and mentor engineers through code and design review.
What You'll Bring — Required
- 7 years building production AI/ML systems, including AWS or cloud-native patterns for AI/ML workloads.
- Strong Python and software engineering best practices (source control, CI/CD, testing, documentation).
- Deep expertise in at least one of: LLM-based systems (fine-tuning, inference optimization, prompt engineering, transformers/vLLM/agentic frameworks); document intelligence/IDP; or ML system design (training pipelines, model serving, evaluation infrastructure).
- Experience operating end-to-end ML pipelines in production (MLOps).
- Solid fundamentals in statistics, experimentation, and data quality, with rigorous reasoning about metrics and the limits of AI systems.
- Track record leading technical design, mentoring engineers, and driving architectural decisions within a team.
- Strong written and verbal communication across technical and non-technical audiences.
Preferred
- Familiarity with agentic architectures and protocols (e.g., MCP, A2A) or multi-step, tool-using workflows.
- LLM cost and latency optimization at scale (quantization, batching, model routing).
- Financial services or FinTech experience, particularly in document-heavy or compliance-sensitive domains.
- Open-source contributions or published technical writing in applied AI.
Compensation
- Base salary $200,000–$250,000 plus bonus and equity.
- Hybrid schedule: in office Monday–Thursday with the flexibility to work remotely on Friday.
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Salary : $200,000 - $250,000