Manager of Artificial Intelligence

Our client’s AI Studio is seeking a hands-on Engineering Manager to lead the engineering execution of their AI systems and platform services. This role combines technical leadership, people management, and hands-on engineering. You will manage a team of AI engineers (U.S. and India) while contributing directly to architecture, code, and delivery of production AI systems.

You will focus on raising engineering rigor, improving system architecture quality, ensuring scalable, reliable AI services, and driving consistent delivery execution. This role reports to the VP, Artificial Intelligence, and partners closely with Product and Domain leaders.

Scope of Responsibility:

• Team technical mentorship
• Delivery predictability
• Offshore team coordination

Responsibilities:

AI Systems Engineering Leadership

• Guide the team in building production-grade RAG pipelines and agentic systems
• Establish practical patterns for multi-model orchestration and inference reliability
• Ensure AI systems include cost controls, fallback strategies, and evaluation frameworks
• Review and improve agent safety, tool invocation patterns, and guardrails
• Lead architecture reviews for new AI services.
• Improve API design standards, versioning practices, and event-driven patterns
• Ensure proper handling of retries, idempotency, and asynchronous workflows
• Drive adoption of observability and monitoring best practices
• Identify and remediate architectural debt.
• Directly manage AI engineers in the U.S.
• Provide technical oversight and structured guidance to offshore India engineers
• Conduct code reviews and system design reviews
• Coach engineers in distributed systems thinking and production hardening
• Improve sprint execution discipline and technical accountability

Delivery & Operational Excellence

• Ensure AI solutions move from prototype to production reliably
• Partner with Product and Domain AI leads to translate requirements into well-architected systems
• Coordinate closely with Dev Ops on deployment, CI/CD, and infrastructure standards
• Improve predictability in delivery timelines and system reliability

Qualifications

• 8 years of professional software engineering experience
• 2+ years developing enterprise AI applications
• 2+ years of people management or technical team leadership
• Hands-on experience building LLM-integrated applications
• Experience with RAG pipelines, vector databases and agentic stacks
• Strong background in backend and distributed systems design