Sr. Manager- AI Platform Lead

• Provide technical leadership and clear, pragmatic product-centric architecture for our AI platforms
• Translate product and business requirements into scalable platform capabilities (agent hosting, LLM serving, gateway/integration architecture, observability and operations)
• Drive platform decisions around LLM serving (model endpoints, caching, batching, latency vs. cost tradeoffs), AI Gateways (routing, policy, rate-limiting, auditing), and agent hosting patterns (single/multi-tenant, sandboxing, lifecycle)
• Own platform reliability, scalability and cost: define SLIs/SLOs, capacity planning, cost attribution and FinOps practices
• Collaborate with Product Owners, Principal Engineers and stakeholders to define the roadmap, acceptance criteria, and delivery milestones
• Lead, coach and grow a high-performing engineering team focused on platform services, integrations (low/no-code tooling such as n8n, and pro-code agent hosting frameworks like AgentCore or equivalents), CI/CD for agents/models, and marketplace features
• Establish standards for security, compliance and model governance (data handling, access controls, logging and auditability), particularly for regulated environments
• Be hands-on when needed — prototype architectures, review designs, troubleshoot production incidents, and participate in code/design reviews

• Bachelor's degree in computer science, Engineering, or equivalent practical experience with a total 12-17 years of industry experience
• 8+ years of engineering experience building/platforming cloud services or developer platforms, with 3+ years leading engineering teams or technical programs
• Proven experience designing and operating cloud-native platforms (Kubernetes, containers, microservices, service meshes)
• Hands-on experience with LLM serving or model-serving patterns (hosting models, request routing, batching, scaling, latency/cost tradeoffs) — or adjacent experience (large-scale inference endpoints, model CI/CD)
• Practical knowledge of API/Gateway patterns, authentication/authorization, and secure integrations
• Familiarity with cost attribution and FinOps concepts for compute/AI workloads and toolchains for measuring and controlling model/agent costs
• Strong track record working with product managers and senior technical stakeholders to deliver platform capabilities and roadmaps
• Excellent communication skills: able to explain technical tradeoffs to technical and non-technical audiences
• Experience with observability and SRE practices (metrics, tracing, logging, incident management)

• Master’s degree (or equivalent) in a technical discipline
• Direct experience with agentic-AI platforms, agent hosting frameworks (e.g., AgentCore or similar), and low/no-code orchestration platforms (e.g., n8n or comparable workflow builders)
• Familiarity with LLM ecosystems (OpenAI/Anthropic/Google/Meta deployments, LLM orchestration libraries and tools) and experience with model governance frameworks
• Experience implementing or operating AI Gateways, policy/routing layers or centralized model access control and auditing
• Experience with FinOps tooling (Kubecost, cloud cost tools) and implementing cost-allocation models for platform customers
• Experience in a regulated industry (pharmaceuticals, biotech, healthcare, finance) and understanding of compliance requirements around data handling and audit trails
• Familiarity with AWS ecosystem and internal enterprise tools is a plus (helpful for faster onboarding into existing integrations)
• Experience shipping developer experience features (SDKs, CLI, templates, documentation) that increase adoption and reduce onboarding time

Competitive and comprehensive Total Rewards Plans that are aligned with local industry standards