Director of Applied AI Engineering

We are looking for a strategic and hands-on Director of Applied AI Engineering to build and lead a high-performing team that transforms artificial intelligence capabilities into production-ready business solutions. In this leadership role, you will drive the development and deployment of AI-powered applications that improve operational efficiency, customer experiences, and business performance. You will oversee the full AI delivery lifecycle, from workflow discovery and rapid prototyping to production deployment, evaluation, and ongoing optimization. Working across product, sales, marketing, operations, and risk functions, you will ensure AI solutions address real business needs while meeting rigorous security and compliance standards. You will establish the technical architecture, engineering practices, and responsible AI guardrails needed to scale adoption safely. This is an opportunity to shape an emerging AI engineering function in a fast-paced financial technology environment, combining technical leadership, team development, and measurable business impact.

• Bachelor's degree in Computer Science, Engineering, or a related discipline, or equivalent practical experience.

• At least 10 years of experience in software engineering, machine learning, or a related technical field, including experience in customer-facing or business-embedded engineering roles such as solutions engineering, professional services, or forward-deployed engineering.

• At least 2 years of experience building and deploying production systems powered by large language models (LLMs), including at least one agentic or conversational application used by real customers or business users.

• At least 4 years of hands-on engineering management experience, with a demonstrated track record of hiring, developing, coaching, and mentoring high-performing teams.

• Strong software engineering fundamentals and the ability to personally debug, troubleshoot, and resolve complex issues in production AI systems.

• Practical expertise in code-level AI SDKs and agent frameworks, such as LangGraph, CrewAI, or AWS Strands Agents, with the ability to select appropriate technologies rather than relying exclusively on no-code tools.

• Strong understanding of production AI application architecture, including tool and function calling, retrieval-augmented generation (RAG), vector search, structured outputs, conversation and state management, and the appropriate use of deterministic workflows.

• Cloud engineering experience, preferably with AWS, including managed AI services such as Bedrock AgentCore, containerized deployments, identity and access management (IAM), networking, and cost optimization.

• Proven experience deploying and maintaining production AI systems, with expertise in CI/CD, observability, tracing, incident management, latency optimization, and operational cost controls.

• Experience integrating systematic evaluation into the development lifecycle through test datasets, regression testing, human review, and ongoing measurement of production quality.

• Strong understanding of AI security, including prompt injection defenses, least-privilege permissions, audit trails, and human-in-the-loop controls.

• Familiarity with financial services compliance and security frameworks, including Model Risk Management, SOC 2, PCI DSS, and the Gramm-Leach-Bliley Act (GLBA).

• Exceptional leadership, analytical thinking, and problem-solving skills, with the ability to communicate complex technical concepts clearly to executives, engineering teams, and nontechnical stakeholders.

• Demonstrated ability to balance strategic planning with hands-on execution, manage competing priorities, and deliver results in a fast-moving, evolving business environment.

• Work authorization: Candidates must be eligible to work in the United States, as visa sponsorship is not available for this position.