Director of Applied AI Engineering

Jobgether; United States; Remote; USD 207,000 - 290,000; Full time

Benefits offered by this job

Health coverage

401(k) plan with employer matching

Flexible PTO

Paid parental leave

Mental health support

Education assistance

Lifestyle spending account

Remote work flexibility (US-based)

Leadership opportunities

Job summary

Jobgether seeks an experienced leader to build and guide an Applied AI Engineering team in the United States. You will shape the operating model, standards, and long-term vision, partnering with product and business stakeholders to deliver practical AI solutions.

You will drive rapid discovery, production readiness, and secure, auditable AI implementations across enterprise projects, focusing on risk, compliance, and cost optimization while mentoring engineers and delivering measurable impact.

Qualifications

• Bachelor’s degree or equivalent experience in CS or related field.
• 10+ years in software, ML, or related technical field; customer-facing roles count.
• 2+ years building production systems with LLMs and agentic apps.
• 4+ years of engineering management; hiring and mentoring teams.

Responsibilities

• Build and lead a high-performing Applied AI Engineering team.
• Partner with product teams and stakeholders to identify automation opportunities.
• Lead rapid discovery, prototyping, and transition to production systems.
• Architect and maintain production-grade AI applications and tools.
• Design secure, modular AI integrations with least-privilege access.
• Own evaluation lifecycle with benchmarks, tests, and human-in-the-loop.
• Collaborate on CI/CD, observability, tracing, and incident response.
• Ensure AI solutions are explainable, auditable, and compliant.
• Define governance, guardrails, and reference architectures.
• Provide regular leadership updates with measurable business impact.
• Hands-on troubleshooting and removal of engineering bottlenecks.

Skills

Leadership

Analytical thinking

Problem solving

Communication

Production AI systems

Debugging production issues

AI security

Explainable AI

Education

Bachelor's degree or equivalent

Tools

LangGraph

CrewAI

AWS Strands Agents

Bedrock AgentCore

Job description

Accountabilities

• Build and lead a high-performing Applied AI Engineering team, establishing its operating model, engagement strategy, technical standards, and long-term vision.
• Partner with product teams, customers, and business stakeholders to understand existing workflows, identify opportunities for automation, and translate operational challenges into practical AI solutions.
• Lead rapid discovery, experimentation, and prototyping, ensuring successful concepts transition efficiently into reliable production systems across multiple enterprise projects.
• Architect, develop, deploy, and maintain production-grade AI applications, including agentic workflows, chatbots, copilots, agent-assist tools, and traditional machine learning models.
• Design modular, reusable integrations that enable AI agents to securely access enterprise systems and internal data while enforcing least-privilege access controls.
• Own the AI evaluation lifecycle by establishing benchmark datasets, offline and online evaluations, regression testing, human-in-the-loop validation, and production-readiness criteria.
• Collaborate with cloud and data engineering teams to manage production operations, including CI/CD pipelines, observability, tracing, incident response, latency management, and inference cost optimization.
• Work closely with Risk, Compliance, Legal, and Information Security teams to ensure AI solutions are explainable, auditable, secure, and aligned with applicable financial services regulations.
• Define responsible AI principles, security guardrails, governance processes, and technical architectures that support sustainable and compliant AI adoption.
• Provide regular updates to senior leadership through clear reporting, performance indicators, and measurable assessments of business impact.
• Stay hands-on with technical execution, troubleshooting complex production issues, resolving engineering bottlenecks, and guiding the team through critical implementation challenges.
• Foster a culture of technical excellence, continuous learning, accountability, and collaboration while recruiting, coaching, and developing engineering talent.

Requirements

• 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.

Benefits

• Competitive compensation: Annual salary range of $207,000–$290,000 , depending on work location, relevant experience, technical expertise, and qualifications.
• Health coverage: Medical, dental, and vision insurance.
• Retirement planning: 401(k) plan with employer matching contributions.
• Work-life balance: Flexible paid time off.
• Family support: Paid maternity and paternity leave.
• Mental health and wellness: Access to mental health support and well-being programs.
• Professional development: Education assistance to support continued learning and career growth.
• Lifestyle benefits: Company-funded lifestyle spending account.
• Remote flexibility: US-based remote work arrangement.
• Leadership opportunities: The chance to build an Applied AI engineering function, establish technical standards, and influence the adoption of AI across a growing financial technology organization.