Engineering Manager AI

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for an Engineering Manager AI based in United States .

This is a hands-on engineering leadership opportunity focused on building intelligent systems that optimize payment performance and power AI-driven products. You'll lead a team of approximately 7--8 engineers across AI/ML, backend, and platform engineering. The role combines people leadership with enough technical depth to guide architecture, challenge technical decisions, and help unblock complex engineering problems. You'll own delivery across scoping, costing, sequencing, and execution while establishing strong standards for quality and engineering practices.

You'll work closely with Product, Operations, Modeling, and senior leadership to turn business priorities into an actionable technical roadmap. The environment is fast-moving, multicultural, and startup-oriented, with a strong emphasis on innovation, ownership, and continuous growth.

Accountabilities:

• Lead and grow a team of AI/ML, backend, and platform engineers, including hiring, performance management, coaching, retention, and career development.
• Coach engineers through technical designs, architecture decisions, code reviews, and complex engineering trade-offs.
• Establish and maintain high engineering standards across code quality, testing, CI/CD, and development practices.
• Own delivery planning for the team, including accurate costing, estimation, sequencing, prioritization, and accountability for commitments.
• Guide architecture for ML model lifecycle processes, including training, evaluation, monitoring, and retraining.
• Oversee LLM-powered workflows such as agent orchestration, RAG pipelines, vector database integrations, and related AI systems.
• Provide technical oversight for inference services supporting live payment routing, ensuring strict latency, reliability, and scalability requirements are met.
• Ensure AWS infrastructure, CI/CD, observability, dashboards, tracing, and on-call practices meet strong reliability and operational standards.
• Apply appropriate PCI-DSS and data-handling considerations to systems and services that interact with payment data.
• Translate product vision and business priorities into executable technical roadmaps with clear timelines, scope, and trade-offs.
• Partner closely with Product, Operations, and Modeling leadership to maintain alignment and create short feedback loops.
• Represent the AI/ML engineering team's progress, priorities, risks, and blockers to senior leadership.

• 8 years of professional software engineering experience, including 2--3 years managing and leading engineering teams.
• Proven experience building and shipping backend and/or machine learning systems at scale.
• Experience hiring, developing, retaining, and managing engineers while balancing people development with delivery objectives.
• Hands-on familiarity with modern AI/ML systems, including model training and serving, LLM-powered workflows, agents, RAG, orchestration, or related technologies.
• Practical experience with LLM-based systems in production, particularly agents, RAG pipelines, or AI workflow orchestration.
• Strong technical understanding of backend systems, distributed architectures, APIs, and production engineering practices.
• Experience with technologies such as Go, Python, gRPC, REST APIs, event streaming, and distributed systems is valuable.
• Familiarity with AWS infrastructure and services, including ECS/EKS, Terraform, RDS/Aurora, and S3.
• Experience with AI/ML technologies such as PyTorch, Tensor Flow, XGBoost, scikit-learn, MLflow, or Weights & Biases is beneficial.
• Knowledge of LLM and agent technologies such as Lang Graph, Lang Chain, RAG, vector databases, prompt engineering, and LLM evaluation is valuable.
• Familiarity with observability technologies and practices, including Prometheus, Grafana, Open Telemetry, structured logging, and on-call runbooks.
• Payments, fintech, or experience in another regulated and latency-sensitive industry is a strong plus, including familiarity with PCI-DSS, tokenization, or payment service provider integrations.
• Strong communication and stakeholder management skills, with the ability to communicate clearly with both technical and non-technical partners.
• Comfortable operating in a rapidly changing startup environment, with the ability to adapt scope, priorities, and communication while maintaining team trust.
• Strong growth mindset, self-awareness, and…