Staff Engineer - AI Platform

Accountabilities:

• Architect, develop, and maintain production-grade agentic systems featuring multi-step reasoning workflows, tool usage, orchestration, planning, memory and context management, and human-in-the-loop controls.

• Build and evolve a shared AI platform for product and engineering teams, including reusable agent frameworks, tool integrations, Model Context Protocol (MCP) integrations, LLM gateways, and prompt and model versioning.

• Design and implement LLM gateway capabilities such as model routing, rate limiting, caching, fallback strategies, and usage controls.

• Establish comprehensive AI evaluation and observability practices, including evaluation frameworks, regression testing, agent tracing, and production metrics for quality, latency, cost, and safety.

• Lead the adoption of AI-driven engineering workflows, developer tooling, and automation that improve engineering velocity and reduce repetitive operational work.

• Manage cloud infrastructure for AI workloads across Google Cloud Platform (GCP) and/or AWS, including provisioning, scaling, security, access management, and cost governance.

• Implement robust AI safety and reliability measures, including guardrails, scoped permissions, sandboxing, prompt-injection defenses, audit trails, graceful degradation, and incident management practices.

• Drive continuous optimization of inference, token usage, and infrastructure costs while maintaining high standards of performance, reliability, and output quality.

• Define technical architecture and engineering standards, lead design reviews, produce design documentation, and mentor engineers across teams.

• Collaborate with product engineering, site reliability engineering, infrastructure, security, and organizational leadership to align AI platform capabilities with business and technical priorities.

• Use measurable performance data to guide architectural decisions, prioritize improvements, communicate risks, and report on platform health, agent quality, and cost efficiency.

• Champion an AI-first engineering culture, ensuring teams adopt shared AI capabilities and achieve measurable improvements in productivity and operational effectiveness.

Requirements:

• At least 12 years of overall software engineering experience, including substantial experience as a senior or staff-level individual contributor.

• At least one year of recent experience architecting and operating production agentic systems, including multi-step tool-using agents, orchestration, memory and context management, and safety guardrails.

• An AI-first mindset, with active use of AI tools in personal engineering workflows and the ability to encourage adoption across an engineering organization.

• Expert-level proficiency in at least one of the following programming languages: Go, Python, or Node.js.

• Strong hands-on engineering experience, including writing and reviewing code, debugging production issues, and making deep architectural contributions. Candidates whose work has been exclusively managerial rather than hands-on during the past year are not a fit for this role.

• Strong understanding of LLM application fundamentals, including tool and function calling, MCP, retrieval, context engineering, prompt and model management, and evaluation methods for nondeterministic systems.

• Extensive experience with observability practices, particularly AI observability, including tracing agent executions and monitoring production quality, latency, cost, and safety. Familiarity with Prometheus, Grafana, ELK/OpenSearch, or equivalent tools is expected.

• Deep knowledge of distributed systems architecture, including consistency, availability, fault tolerance, scalability, and large-scale system design trade-offs.

• Demonstrated experience optimizing costs in enterprise cloud environments, including managing LLM inference and token spend through model routing, caching, and budget controls.

• Practical experience managing GCP and/or AWS services, including infrastructure provisioning, scaling, identity and access management (IAM), networking, and cost governance.

• At least three years of experience as an individual contributor on platform engineering teams operating at enterprise scale.

• Proven ability to lead through technical influence, including writing design documents, aligning cross-functional teams, setting engineering standards, and mentoring other engineers.

• Strong analytical and data-driven decision-making skills, with experience using performance metrics to guide priorities and report outcomes.

• Excellent written and verbal communication skills, with the ability to influence technical and non-technical stakeholders.

• Preferred: Open-source contributions or published work related to agentic systems.

Benefits:

• Remote working arrangement in India.

• Professional growth opportunities through technical leadership and ownership of enterprise-scale AI platform initiatives.

• Opportunities to work on advanced AI agents, cloud infrastructure, distributed systems, and developer productivity tooling.

• Collaboration with a globally distributed engineering organization and cross-functional technical teams.

• Opportunity to shape AI engineering standards, mentor engineers, and influence platform architecture and strategy.