Manager - AI Platform Engineer

RTX; Bengaluru; Hybrid; INR 2,800,000 - 4,600,000; Full time

Job summary

RTX is seeking an experienced Backend Platform Engineer to design, develop, and own scalable backend services, APIs, and distributed platform capabilities that support enterprise AI applications.

You will lead design and integration of AI platform capabilities including gateways, model access, and agent runtimes, while ensuring secure patterns, observability, and robust deployment across cloud-native environments.

Qualifications

• Bachelor's degree in CS/Software/Engineering.
• Minimum 8 years software or platform engineering experience with backend systems.

Responsibilities

• Design, develop, and own scalable backend services, APIs, and distributed platform capabilities.
• Lead design and integration of AI platform capabilities and gateways.
• Define deployment patterns using containers, Kubernetes, CI/CD, and IaC.
• Build observability, monitoring, and operational support for distributed services.
• Mentor engineers and collaborate across globally distributed teams.

Skills

Python

Java

Go

C#

API design

Distributed systems

Mentorship

Cloud platforms

Security concepts

Leadership

Education

Bachelor's Degree in Computer Science

Bachelor's Degree in Software Engineering

Engineering degree

Tools

Docker

Kubernetes

CI/CD

Infrastructure as Code

Job description

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.

Role Type

• Onsite: Employees who are working in Onsite roles will work primarily onsite. This includes all production and maintenance employees, as they are essential to the development of our products.
• 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.
• Remote: Employees who are working in Remote roles will work primarily offsite (from home). If you live within a reasonable commute of an RTX site with other colleagues you interact with, your manager will discuss whether there is a degree of onsite presence associated with this role. 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.

Join RTX Enterprise Services and help shape the enterprise AI platform used by teams across a global aerospace and defense company. You will combine senior software engineering, cloud platform, and AI infrastructure expertise to build reusable services that influence how AI is deployed and operated across RTX.

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.

Equal Employment Opportunity Statement

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.

RTX is an aerospace and defense company that provides advanced systems and services for commercial, military and government customers worldwide. It comprises three industry-leading businesses – Collins Aerospace, Pratt & Whitney, and Raytheon. Its 195,000 employees enable the company to operate at the edge of known science as they imagine and deliver solutions that push the boundaries in quantum physics, electric propulsion, directed energy, hypersonics, avionics and cybersecurity. The company, formed in 2020 through the combination of Raytheon Company and the United Technologies Corporation aerospace businesses, is headquartered in Arlington, Virginia.