AI Native Software Engineering Manager

Overview In this role you will design and deploy enterprise-ready AI agents and agentic workflows at scale, embedding with clients to turn experiments into production systems. You will shape cross-functional technical playbooks and collaborate with ecosystems partners to deliver robust, secure solutions. You’ll drive measurable outcomes in AI-assisted processes across industries while advancing AI-native engineering practices. This role combines hands-on engineering, client advisory, and strategic influence to accelerate enterprise adoption of AI-native workflows. Compensation / Benefitsmedical, dental, visionlife and long-term disability401(k) planbonus opportunitiespaid holidays and paid time off ResponsibilitiesDesign enterprise-ready AI agents including orchestration, tool invocation, and lifecycle observabilityDevelop abstraction layers across AI providers to enable multi-provider pipelinesBuild cloud-native AI systems with Kubernetes, Docker, microservices, serverless, CI/CD, and observabilityTailor agentic applications for vertical domains (finance, healthcare, retail)Lead client design workshops, POCs, and code-with sessions to shape data-driven workflowsDefine metrics, create test harnesses, and plan evaluations for accuracy, latency, safety, and costCreate reusable patterns and documentation to influence assets and roadmapsTravel as required to engage with clients Key requirements3+ years of cloud-native systems engineering (APIs, microservices, containerization, serverless)1+ year designing and deploying agentic solutions in production2+ years experience with AI platforms (OpenAI, Claude, Vertex AI) and multi-provider pipelines5+ years programming in Python, Java, or equivalent; familiarity with evaluation tooling, logging, monitoring, and observability5+ years production deployment experience (CI/CD, Terraform/Helm, monitoring)5+ years experience with client communication and leading workshops under ambiguityBachelor’s degree or equivalent work experiencecritical thinking in ambiguitystrong collaboration with stakeholderseffective storytelling in workshopsagent architecture and orchestrationRAG and context engineeringretrieval, tool invocation, evaluation harnesses