Director of Applied AI Engineering
New
J
JobgetherFinancial technology
Based in the United StatesFull-TimeDirector
Salary207,000 - 290,000 USD per year
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Job Details
- Experience
- At least 10 years of experience in software engineering, machine learning, or a related technical field; at least 2 years building and deploying production systems powered by large language models; at least 4 years of hands-on engineering management experience.
- Required Skills
- CI/CDLLM
Requirements
- Bachelor's degree in Computer Science, Engineering, a related discipline, or equivalent practical experience.
- At least 10 years of experience in software engineering, machine learning, or a related technical field.
- Customer-facing or business-embedded engineering experience, such as solutions engineering, professional services, or forward-deployed engineering.
- At least 2 years building and deploying production systems powered by large language models, including an agentic or conversational application used by customers or business users.
- At least 4 years of hands-on engineering management experience, including hiring, developing, coaching, and mentoring teams.
- Strong software engineering fundamentals and ability to debug and resolve complex production AI issues.
- Practical expertise with code-level AI SDKs and agent frameworks such as LangGraph, CrewAI, or AWS Strands Agents.
- Knowledge of production AI architecture, including tool and function calling, RAG, vector search, structured outputs, conversation and state management, and deterministic workflows.
- Cloud engineering experience, preferably AWS, including managed AI services such as Bedrock AgentCore, containerized deployments, IAM, networking, and cost optimization.
- Experience with production AI systems, CI/CD, observability, tracing, incident management, latency optimization, and operational cost controls.
- Experience integrating evaluation into development through test datasets, regression testing, human review, and ongoing production-quality measurement.
- 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 GLBA.
Responsibilities
- Build and lead the Applied AI Engineering team, defining its operating model, engagement strategy, technical standards, and vision.
- Partner with product teams, customers, and business stakeholders to understand workflows and identify AI automation opportunities.
- Lead discovery, experimentation, and prototyping, moving successful concepts into production systems.
- Architect, develop, deploy, and maintain AI applications, including agentic workflows, chatbots, copilots, agent-assist tools, and machine learning models.
- Design reusable integrations that provide AI agents secure access to enterprise systems and internal data.
- Establish benchmark datasets, evaluations, regression testing, human review, and production-readiness criteria.
- Coordinate with cloud and data engineering teams on CI/CD, observability, tracing, incident response, latency, and inference costs.
- Work with Risk, Compliance, Legal, and Information Security to ensure AI solutions are explainable, auditable, secure, and compliant.
- Define responsible AI principles, security guardrails, governance processes, and technical architectures.
- Report performance indicators and business impact to senior leadership, while coaching and developing engineering talent.
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