Principal Agentic Engineer, AI Ecosystem

Accountabilities: Architect across the AI ecosystem: Maintain a system-wide architectural perspective across AI Platforms, AI Developer Experience (AI DevX), and Agentic Product Development Lifecycle (PDLC), ensuring that investments and technical capabilities work together effectively. Define the technical vision and architecture for an AI-native developer ecosystem, enabling teams to build on shared primitives rather than independently solving the same problems. Lead build-versus-buy decisions for PDLC-critical agents and ecosystem-wide capabilities, evaluating whether external solutions can deliver greater value than internal development. Design and build foundational ecosystem components, including agent execution frameworks, context and retrieval systems, and telemetry that treats token consumption and cost as core design considerations. Own cross-team foundational capabilities: Define what constitutes a reusable agent, including its unit structure, interfaces, and required standards. Establish agent standards covering human-in-the-loop requirements, blast-radius policies, evaluation expectations, and promotion gates, partnering with AI Platforms for long-term infrastructure operations. Design the non-human identity and agent authorization (authZ) model required to support scheduled and unattended agents across teams. Build shared AI evaluation and regression infrastructure, including frameworks, CI integrations, and result storage, avoiding fragmented or duplicated implementations. Deliver hands-on engineering: Write and ship production code for high-impact foundational components rather than limiting involvement to architecture recommendations or technical specifications. Work closely with active initiatives, such as on-call investigation agents, to validate architectural decisions under real-world usage and economic constraints. Prototype and de-risk technically challenging components before requesting engineering investment from other teams. Lead through technical influence: Mentor Agentic Engineers and Staff Engineers across AI Platforms, AI DevX, and Agentic PDLC in agent architecture, context engineering, and evaluation practices. Help strengthen the organization's senior technical capabilities through knowledge sharing and engineering guidance. Produce architecture decision records, technical standards, and implementation playbooks that allow individual teams and their agents to build independently against a consistent architecture. Represent a clear technical perspective in roadmap and build-versus-buy discussions, influencing decisions across teams without relying on formal management authority. Partner closely with AI Platforms, AI DevX, Agentic PDLC, and Product leadership as a technical counterpart, with ownership focused on the shared challenges between these groups. Requirements 12+ years of software engineering experience, including substantial work in platform or systems architecture. Experience designing and delivering complex systems at consumer scale, preferably supporting 50 million or more monthly active users or an equivalent level of scale. Demonstrated experience architecting and deploying production agentic systems, including designing the frameworks, identity models, and evaluation infrastructure on which other agents operate. Direct experience with non-human identity, service-account management, or agent authorization models, or a well-reasoned technical approach to designing and implementing them. Experience designing and consuming structured interfaces for AI systems, including MCP-style tool definitions, JSON Schema, and OpenAPI. Understanding of tool-calling architectures and contract-first approaches to agent development. Proven ability to build evaluation and regression frameworks for AI systems, including promotion gates, scoring mechanisms, and CI integration—not simply running ad hoc evaluations. Strong architectural judgment across multiple services and engineering teams, with the ability to distinguish problems that require shared platform investment from those better addressed independently. Strong understanding of AI economics, including token costs as an architectural constraint, progressive retrieval strategies, and the trade-offs between centralized and distributed model usage. Demonstrated ability to influence technical direction across teams without direct reporting authority, using documentation, prototypes, and measurable engineering outcomes. Excellent written communication skills, including the ability to produce precise architecture decision records, technical standards, and playbooks that other engineers and AI agents can follow directly. Ability to balance strategic technical leadership with hands-on implementation and production delivery. Preferred qualifications: Experience building or operating an agent orchestration or deployment platform. Experience developing or managing an MCP-style connector gateway. Background in machine learning infrastructure, model observability, or production AI systems. Experience working at an organization that has undergone a comparable AI-native transformation. Mobile engineering experience, particularly iOS or Android, or experience with high-throughput cloud systems. Understanding of how agent architecture must adapt to different environments, including mobile applications and large-scale cloud infrastructure. Benefits Salary: US-based annual salary range of $247,000–$363,000. Final compensation considers background, job-related knowledge, skills, experience, and geographic location across three US pay tiers. Equity: Restricted Stock Units (RSUs) as part of total compensation. Healthcare: Medical, dental, and vision insurance premiums fully covered for employees. Insurance protection: Life and disability insurance plans. Retirement: 401(k) plan with a company matching program. Family support: Paid parental leave. Time off: Flexible paid time off, companywide holidays, and summer and winter shutdown periods. Well-being: Mental Wellness Program and Employee Assistance Program (EAP). Professional development: Learning and development programs to support continued technical and career growth. Remote-work support: Equipment, tools, and reimbursement support for a productive remote working environment. Membership and product perks: Complimentary premium membership and tracking devices. Remote-first culture: Opportunity to work remotely within the United States as part of a distributed engineering organization. How Jobgether works: We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team. We appreciate your interest and wish you the best! Why Apply Through Jobgether? Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). 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