Position Overview

A financial technology firm is seeking a Vice President, Artificial Intelligence Engineer to lead the design, buildout, and production delivery of AI systems that support business automation, document-heavy workflows, knowledge systems, and intelligent orchestration.

This role is suited for a senior engineer who has delivered complex AI/ML systems from concept through production and can combine hands-on engineering depth with technical leadership, architecture ownership, stakeholder partnership, and team mentorship. The individual will own key AI workstreams, guide system design decisions, and help translate ambiguous business needs into scalable, measurable, production-grade solutions.

Mandatory Requirements:

7+ years of experience building production AI/ML systems.
Hands-on experience with AWS or cloud-native AI/ML development patterns.
Proven track record delivering complex systems from initial scoping through production deployment.
Strong Python proficiency and ability to build maintainable, well-structured software.
Experience with source control, CI/CD, testing, documentation, and software engineering best practices.
Deep expertise in at least one of the following:
LLM-based systems, including fine-tuning, inference optimization, prompt engineering, transformers, vLLM, or agentic frameworks.
Document intelligence / intelligent document processing.
ML system design, including training pipelines, model serving, and evaluation infrastructure.
Experience designing and operating production ML pipelines, including training, deployment, monitoring, and iteration.
Strong understanding of statistics, experimentation, data quality, metrics, error analysis, and AI system limitations.
Experience leading technical design, mentoring engineers, and driving architecture decisions.
Strong written and verbal communication skills with technical and non-technical stakeholders.

Key Responsibilities

Lead architecture and delivery of production AI systems, including document intelligence, knowledge systems, and agentic orchestration for workflow automation.
Own AI initiatives from business problem definition through technical design, implementation, deployment, monitoring, and continuous improvement.
Define technical approaches for APIs, system decomposition, infrastructure patterns, evaluation strategy, and platform standards.
Build and improve evaluation frameworks for AI systems, including metrics, benchmark datasets, versioning, reproducibility, and quality tracking.
Partner with Product, Operations, Legal, Business, and other cross-functional teams to identify AI use cases, define requirements, and communicate risks, tradeoffs, and recommendations.
Mentor engineers through code review, design review, technical problem-solving, and knowledge sharing.
Identify recurring technical or process issues and recommend improvements to tooling, infrastructure, team workflows, and development velocity.
Ensure AI capabilities are reliable, scalable, maintainable, measurable, and aligned with business outcomes.

Required Qualifications

7+ years of hands-on experience developing and deploying production AI/ML systems.
Strong Python engineering skills and experience building production-quality software.
Experience with AWS or similar cloud-native environments for AI/ML workloads.
Practical experience with MLOps, including model training, deployment, monitoring, iteration, and operational support.
Deep technical expertise in LLM systems, document intelligence, intelligent document processing, or ML platform/system design.
Strong foundation in statistical reasoning, experimentation, data quality, performance metrics, and model evaluation.
Ability to make architectural decisions, set technical direction, and raise engineering standards across a team.
Experience working directly with business stakeholders to convert unclear or evolving needs into executable technical plans.
Strong documentation, presentation, and communication skills.

Preferred Qualifications

Experience across more than one of the following areas: LLM systems, document intelligence, ML platforms, or AI infrastructure.
Familiarity with agentic architectures, MCP, A2A, or multi-step tool-using AI workflows.
Knowledge of LLM inference cost and latency optimization, including quantization, batching, model routing, or related strategies.
Prior experience in financial services, FinTech, compliance-sensitive environments, or document-intensive business domains.
Open-source contributions or published technical writing related to applied AI, ML systems, or AI engineering.

Why Join This Team / Organization Summary

This role offers the opportunity to lead high-impact AI engineering work in a production environment where AI systems are expected to solve real business problems at scale. It is a strong fit for a senior engineer who wants to remain hands-on while influencing architecture, mentoring others, and delivering measurable AI capabilities across business-critical workflows.