Date Posted:
2026-11-03


Country:
India


Location:
IN-KA-BENGALURU-NORTHGATE ~ Sy No 2/2 Venkatala Village ~ SY NO 2/2 VENKATALA VILLAGE, Yelahanka Hobli


Position Role Type:
Hybrid
At RTX, the world's largest aerospace and defense company, 185,000 great minds are united by purpose and inspired to make a difference solving the world’s most complex problems. With our three market leading businesses, world-class operations and investments in research and development, we offer capabilities and opportunity no one else can. Together, we push the boundaries of known science and find new ways to connect and protect our world. Join us and help shape the future of aerospace and defense.
What You Will 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 Will Learn
- How enterprise AI platforms enable Generative AI, machine learning, and agentic solutions to operate securely and consistently across a global aerospace and defense company.
- How models, agents, tools, identity, data, evaluation, governance, and observability come together in a production enterprise AI platform.
- How agentic systems introduce new platform engineering challenges around identity, permissions, tool execution, lifecycle management, runtime controls, and observability.
- How enterprise AI platforms are architected and operated across commercial cloud, hybrid, on-premises, and restricted computing environments.
- How emerging technologies such as AI gateways, agent runtimes, MCP, orchestration frameworks, and AI observability platforms are evaluated and translated into enterprise capabilities.
- How to influence global platform direction by turning recurring needs across RTX business units into reusable software and platform services
Qualifications You Must Have
- A Bachelor's Degree in Computer Science, Software Engineering, Engineering.
- Minimum of 8 years of experience and a 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.
Qualifications We Prefer
- 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.
Learn More & Apply Now
Please ensure the role type defined below is appropriate for your needs before applying to this role. This position is classified as:
Hybrid: Employees who are working in Hybrid roles will work regularly both onsite and offsite. Ratio of time working onsite will be determined in partnership with your leader.
Candidates will learn more about role type and current site status throughout the recruiting process. For onsite and hybrid roles, commuting to and from the assigned site is the employee’s personal responsibility.
Requires advanced to expert knowledge of work area typically obtained through advanced education combined with experience. May have broad knowledge of project management.
Requires substantial knowledge of RTX projects, programs or systems in order to provide enhancements within job area.
Typically requires: A University Degree or equivalent experience and minimum 8 years prior relevant experience, or An Advanced Degree in a related field and minimum 5 years experience
Engineering/Other Technical Positions: Typically requires a degree in Science, Technology, Engineering or Mathematics (STEM) and a minimum of 8 years of prior relevant experience unless prohibited by local laws/regulations.
RTX adheres to the principles of equal employment. All qualified applications will be given careful consideration without regard to ethnicity, color, religion, gender, sexual orientation or identity, national origin, age, disability, protected veteran status or any other characteristic protected by law.
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