Manager - AI Platform Engineer

Overview

Lead the design, build and ownership of scalable backend services and platform capabilities that support enterprise AI applications, models and agents within RTX Enterprise Services.

What you'll do

• Design, develop, and own scalable backend services, APIs, microservices, and distributed platform capabilities that support enterprise AI applications, models, and agents.
• Lead the design and integration of AI platform capabilities including AI gateways, model access and routing, agent runtimes, registries, lifecycle management, model serving, retrieval services, tool integration, and other reusable AI services.
• Design secure platform patterns for agent and application identity, authentication and authorization, secrets management, permissions, tool access, and enterprise system integration.
• Define and implement deployment patterns across development, test, and production environments using containers, Kubernetes, CI/CD, infrastructure-as-code, and modern cloud-native engineering practices.
• Establish observability and reliability capabilities including logging, metrics, distributed tracing, monitoring, alerting, execution telemetry, operational diagnostics, and AI workload cost visibility.
• Build reusable capabilities supporting AI evaluation, model and agent lifecycle management, configuration, versioning, MLOps, and production operations.
• Drive technical decisions for scalability, availability, resilience, security, performance, and support across cloud, hybrid, on-premises, and restricted computing environments.
• Partner with AI Architecture, Applied AI, Application Engineering, Cybersecurity, Data, and product teams, mentor engineers, and translate recurring solution needs into reusable enterprise platform capabilities.

What you'll need

• A Bachelor's Degree in Computer Science, Software Engineering, or Engineering.
• Minimum of 8 years of experience.
• Minimum of 5 years of hands-on software or platform engineering experience developing backend services, APIs, distributed systems, cloud platforms, or production software, including experience owning complex capabilities from design through production.
• Strong hands-on programming experience using Python, Java, Go, C#, or another modern backend programming language, with experience designing tested, maintainable, production-grade software.
• Experience designing APIs, microservices, distributed services, event-driven systems, shared backend capabilities, and integration patterns for large-scale enterprise systems.
• Hands-on experience with Docker, Kubernetes, CI/CD, infrastructure-as-code, and cloud-native deployment using at least one major public cloud platform.
• Experience designing production observability, reliability, monitoring, tracing, alerting, troubleshooting, and operational support for distributed services.
• Strong understanding of enterprise security concepts including identity and access management, authentication and authorization, secrets management, networking, and secure application integration.
• Demonstrated ability to make technical design decisions, lead complex engineering work, mentor other engineers, and collaborate across globally distributed technical teams.

Nice to have

• Experience building AI/ML platforms, developer platforms, internal platforms, or shared enterprise software services used by multiple engineering teams.
• Experience with AI gateways, model serving, inference platforms, model routing, agent runtimes, orchestration platforms, or model and agent lifecycle capabilities.
• Experience with agentic AI platform concepts including agent registration, tool execution, Model Context Protocol (MCP), agent identity, permissions, state, lifecycle management, or agent observability.
• Experience with vector databases, enterprise search, retrieval services, knowledge platforms, feature stores, model registries, or other AI/ML infrastructure.
• Experience with MLOps, model deployment, AI evaluation infrastructure, model or agent versioning, monitoring, or lifecycle automation.
• Experience designing and operating highly available Kubernetes or distributed platforms with resilience, scalability, disaster recovery, and operational excellence requirements.
• Experience designing platform capabilities for hybrid cloud, on-premises, restricted, regulated, or enterprise environments with complex security and integration requirements.
• Experience using AI-assisted development tools and engineering workflows and helping other engineers adopt them effectively.

Details

• Location: Bengaluru, Karnataka, India (Northgate, Sy No 2/2 Venkatala Village, Yelahanka Hobli).
• Work mode: Hybrid, with onsite and offsite time determined in partnership with the leader.
• Commuting to and from the assigned site is the employee's personal responsibility.