Senior Manager, AI Engineering
New
United StatesFull-TimeManager
Salary223,600 - 299,100 USD per year
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Job Details
- Experience
- 8+ years of software engineering experience with at least 4+ years in engineering leadership
- Required Skills
- SQLMachine LearningSaaSLLMDistributed Systems
Requirements
- 8+ years of software engineering experience with at least 4+ years in engineering leadership roles or major technical ownership responsibilities.
- Strong hands-on engineering background with the ability to review code, debug distributed systems, and evaluate system behavior in production environments.
- Proven experience building or shipping AI, ML, platform, infrastructure, workflow automation, or developer platform systems in production.
- Deep understanding of modern LLM-based architectures, including prompt orchestration, tool calling, retrieval-augmented generation, memory systems, and agent workflows.
- Strong expertise in distributed systems, event-driven architectures, SaaS platforms, observability, and multi-tenant system design.
- Experience with production AI safety mechanisms such as permissioning, approvals, audit logs, structured tool execution, and failure handling.
- Strong evaluation mindset, including experience designing test scenarios, regression suites, simulation environments, and monitoring non-deterministic systems.
- Solid data intuition across structured and unstructured data, including SQL, vector search, metadata, and data provenance concepts.
- Excellent communication and leadership skills with the ability to align engineering, product, security, and executive stakeholders.
- Ability to operate effectively in ambiguous environments and translate strategy into clear technical execution plans.
Responsibilities
- Lead the design and delivery of the Agent OS platform, including agent runtime, workflow execution, planning, tool use, long-running processes, and failure recovery mechanisms that support reliable AI behavior at scale.
- Build and evolve context and memory systems, including retrieval pipelines, tenant-aware memory, provenance tracking, and replayable evidence structures for agent decision-making.
- Define and implement the capability platform that standardizes reusable agent functionalities, including tools, prompts, policies, evaluation frameworks, and rollout controls.
- Develop the action and trust layer ensuring secure, governed, and auditable execution of agent actions with approvals, permissions, idempotency, and rollback capabilities.
- Drive evaluation and observability systems, including offline/online testing, simulation environments, regression detection, telemetry, and quality gates for AI behavior.
- Partner closely with cross-functional teams to integrate AI agents into enterprise systems while ensuring safety, reliability, and compliance with business rules and data governance standards.
- Guide architectural decisions and hands-on engineering work, including code reviews, debugging production issues, system design, and technical tradeoffs in model usage, latency, and cost optimization.
- Mentor and develop engineers while fostering a culture of technical excellence, ownership, and fast, high-quality delivery in a rapidly evolving AI environment.
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