Sr Tower Lead (Support & Operations)

About the Role

Sr Tower Lead (Support \& Operations) Gautam Buddha Nagar, Uttar Pradesh **Job Summary** --------------- **Senior Forward Deployment Engineer – GCP Agentic AI** **Job Type**Full\-Time **Experience**8–12 years overall; at least 3 years in Generative AI / Agentic AI with significant enterprise production delivery on Google Cloud **Locations**Noida, Hyderabad, Chennai, Bangalore, Pune **Primary Focus**Solution architecture, customer technical leadership, production strategy, technical governance and FDE leadership **Role Overview** ================= We are looking for a Senior Forward Deployment Engineer (FDE) to lead the technical architecture and end\-to\-end deployment of enterprise Generative AI and Agentic AI solutions for strategic customers on Google Cloud. The Senior FDE is a customer\-facing solution architect who can code: they own the technical success of the customer engagement, make architecture and deployment decisions, lead customer workshops, review implementation, drive production readiness, and mentor FDEs. * Customer\-facing solution architect who can code * Owns technical success of the customer engagement * Makes architecture and production decisions * Leads FDEs and influences the platform roadmap **Typical time allocation** * \~50% architecture and technical leadership * \~30% customer leadership * \~20% engineering oversight and mentoring **Key Responsibilities** ------------------------ * Own the overall technical delivery of enterprise Agentic AI deployments from discovery and architecture through production rollout, hypercare, and transition to BAU. * Lead customer discovery, technical workshops, architecture reviews, executive technical discussions, solution demonstrations, POCs, pilots, and production planning. * Design customer\-specific AI and cloud architectures using Gemini Enterprise Agent Platform, ADK, Google Cloud services, enterprise data sources, applications, security controls, and operational services. * Define agent architecture including multi\-agent patterns, Planner/Critic/Supervisor/Orchestrator designs, domain agents, Skills, tools, RAG, human\-in\-the\-loop controls, state, and enterprise integrations. * Define the integration architecture for REST APIs, MCP tools, databases, ITSM platforms, monitoring systems, identity systems, messaging systems, and proprietary applications. * Decide which requirements should be implemented through configuration, customer\-specific extensions, or reusable platform capabilities; drive the appropriate engineering path. * Define production deployment architecture across Agent Runtime, Cloud Run, GKE, Cloud Functions, Pub/Sub, Cloud Storage, IAM, Secret Manager, networking, and other appropriate Google Cloud services. * Define production architecture requirements for availability, scalability, security, resiliency, observability, release management, rollback, disaster recovery, and operational support. * Lead security and identity architecture discussions covering IAM, RBAC, service accounts, authentication, authorization, secrets management, data protection, auditability, and network controls. * Define and review production readiness criteria covering functionality, security, performance, reliability, evaluation, observability, governance, supportability, and operational readiness. * Define the customer AgentOps strategy including telemetry, tracing, logging, metrics, evaluation, quality monitoring, cost monitoring, and operational dashboards. * Lead complex troubleshooting and root\-cause analysis for production issues involving agents, models, RAG, tools, integrations, networking, authentication, and cloud infrastructure. * Drive reliability, latency, scalability, model performance, and AI cost optimization across customer deployments. * Define and review CI/CD, release management, environment promotion, versioning, rollback, and deployment processes. * Review architecture, integration code, deployment plans, and technical deliverables produced by FDEs; establish engineering quality standards. * Mentor and technically guide FDEs and Associate FDEs working on customer deployments. * Establish reusable deployment patterns, integration patterns, reference architectures, runbooks, onboarding standards, and troubleshooting playbooks. * Identify recurring customer requirements and drive their conversion into reusable agents, Skills, tools, connectors, frameworks, and platform capabilities with Agent Development / Platform Engineering teams. * Act as the senior technical escalation point for complex customer issues and major production incidents. * Provide technical feedback to Product, Agent Development, Platform Engineering, Security, Cloud Architecture, and Operations teams and influence platform roadmap priorities. * Lead technical handover, customer enablement, operational readiness, and transition to support/BAU teams. **Skill Requirements** ---------------------- **Must Have Skills** ==================== * Strong Python and software engineering skills with the ability to review and guide production code. * Extensive hands\-on experience with Generative AI, LLMs, RAG, prompt engineering, tool calling, evaluation, and multi\-agent architectures. * Strong hands\-on experience with ADK and/or LangGraph/LangChain in production. * Strong Google Cloud architecture and engineering experience with Gemini Enterprise Agent Platform / Google Cloud AI services. * Strong enterprise integration experience across REST APIs, databases, ITSM platforms, monitoring systems, identity platforms, and customer\-specific applications. * Strong understanding of IAM, OAuth, service accounts, RBAC, secrets management, authentication, authorization, security, and enterprise networking. * Strong experience with containerized applications, Docker, Cloud Run, GKE, and production deployment patterns. * Strong experience with CI/CD, Git, release management, versioning, and production operations. * Strong experience with observability, tracing, logging, monitoring, debugging, evaluation, and production incident management. * Strong understanding of RAG architecture, knowledge integration, retrieval, evaluation, and agent quality measurement. * Experience with Responsible AI, guardrails, AI security, data protection, and enterprise governance. * Strong customer\-facing communication skills and ability to lead architecture conversations with senior technical stakeholders. **Preferred Skills** ==================== * Deep experience with Gemini Enterprise Agent Platform capabilities including ADK, Agent Runtime, Agent Gateway, Model Armor, Agent Evaluation, and Cloud Observability. * Experience with OpenTelemetry and enterprise AgentOps practices. * Experience with MCP, A2A, and enterprise agent interoperability. * Experience with Terraform / Infrastructure\-as\-Code and enterprise cloud landing zones. * Strong experience with GKE, Cloud Run, Pub/Sub, BigQuery, Cloud Storage, Secret Manager, networking, and private connectivity. * Experience with ServiceNow, ITSM, CloudOps, SRE, AIOps, infrastructure automation, or enterprise operations. * Experience designing highly available, scalable, secure production architectures. * Experience leading POCs, pilots, MVPs, production rollouts, and strategic enterprise deployments. * Exposure to multiple cloud platforms is advantageous. **Other Requirements** ---------------------- **Qualifications** ================== * Bachelor’s or Master’s degree in Computer Science, Engineering, Information Technology, or related field. * 8–12 years of overall software engineering, cloud engineering, solution architecture, or equivalent technical experience. * At least 3 years of practical Generative AI / Agentic AI experience. * Demonstrated track record of delivering multiple enterprise technology or AI solutions into production. * Demonstrated customer\-facing technical leadership and architecture ownership. **Key Attributes** ================== * Strong customer\-facing presence and ability to build technical trust with enterprise customers. * Strong ownership of outcomes rather than individual tasks. * Excellent architecture and problem\-solving skills. * Comfortable operating across AI engineering, cloud architecture, integration, security, and operations. * Able to make pragmatic decisions between reusable platform capabilities and customer\-specific implementation. * Strong mentoring and technical leadership capability.