Lead Senior Backend Engineer (m/f/d)

About the Position

Lead Senior Backend Engineer (m/f/d)

• Full-time

About the Company

T-Systems is part of the Deutsche Telekom Group, with around 30,000 employees worldwide. We create technology with purpose to generate a positive impact on society. We are looking for curious talent, eager to learn, take on challenges, and contribute ideas that transform our customers’ experience.

We trust people: we offer autonomy, continuous support, and a collaborative environment where you can grow without limits. We are one global team, guided by respect, integrity, and a passion for doing better every day.

Role Overview

We are looking for a senior backend engineer highly skilled Python Developer with strong system design skills and experience building production infrastructure. In this role, you will own architect and drive the evolution of the maintain backend platform behind our AI inference endpoints - a multi-tenant system handling authentication, API key management, usage metering, billing, and request proxy services that enable high-availability, data-intensive AI-powered solutions. The role is high-ownership and you will shape architecture decisions, raise the bar of engineering quality, and build systems that are performant, secure, and observable at scale. You’ll collaborate with experts in AI infrastructure and data engineering to build robust, secure, and efficient systems that scale to millions of requests.

Responsibilities

• Design and build core platform services: API gateway, authentication, authorization, key rotation, and multi-tenant isolation.
• Design, implement, and optimize APIs and backend systems using Python frameworks such as primarily FastAPI (or Flask, or Django).
• Architect and implement usage metering, billing integration, and rate limiting for inference endpoints.
• Build and maintain scalable, fault-tolerant microservices for data processing and AI integration.
• Build and operate a high-throughput proxy/routing layer for AI model serving traffic.
• Collaborate with cross-functional teams to design system architecture and ensure seamless system interoperability.
• Design telemetry and observability into the platform from the ground up - structured logging, distributed tracing, metrics, and alerting.
• Implement robust CI/CD pipelines, monitoring, and observability for high-performance production systems.
• Drive technical decisions on architecture, data modeling, and technology choices.
• Identify performance bottlenecks and drive improvements in reliability, scalability, and latency.
• Establish engineering standards for the backend codebase: testing, code review, CI/CD, and deployment practices.
• Ensure best practices for security, compliance, and maintainability across the API lifecycle.
• Collaborate closely with the ML infrastructure team to integrate with model serving systems (e.g., NVIDIA Dynamo, vLLM, SGlang, TensorRT-LLM).

Requirements

Must-have requirements:

• Degree in Computer Science, Software Engineering, or equivalent professional experience.
• 5+ years of experience building and operating backend systems in production, with a meaningful scale with a focus on API design and high-scale production environments.
• Strong proficiency in Python and at least one systems-oriented language (Go, Rust, Java, C++), willingness to work across the current stack which includes Python (FastAPI).
• Proven experience designing and maintaining REST APIs (REST, gRPC, WebSockets, SSE, Webhooks, etc.) in production environments and the tradeoffs between them.
• Solid understanding of distributed systems: consistency, fault tolerance, concurrency, performance, and networking, data pipelines, and performance optimization.
• Experience designing authentication/authorization systems (OAuth2, JWT, API key management, RBAC/ABAC).
• Experience with cloud infrastructure (AWS, GCP, or Azure) and containerized environments (Docker) and orchestration (Kubernetes).
• Understanding of database design (SQL, ORMs, etc.) and data modeling at scale.
• Fluent in English (written and verbal) and comfortable collaborating in international teams.

Nice-to-have requirements:

• Experience with high-throughput, low-latency, or real-time systems (e.g., message queues, event-driven architectures).
• Familiarity with generative AI or large language models (e.g., integration of AI APIs, RAG workflows, or vector databases).
• Experience in modern DevOps practices, infrastructure as code, GitOps workflows, and observability tools (Prometheus, Grafana, OpenTelemetry).
• Exposure to ML serving infrastructure (model servers, GPU scheduling, inference optimization) is a plus but not required.
• Comfortable occasionally touching or contributing to frontend code when necessary.
• Experience in stress/load-testing and evaluating performance of production systems.
• Strong problem-solving mindset and commitment to code quality and performance excellence.
• Track record of improving engineering culture: mentoring junior engineers, introducing better practices, leading technical discussions.

We Offer

Work environment & flexibility

• International, dynamic, and collaborative environment
• T-Social: social initiatives (sports, community, health,...)
• Hybrid work model (remote/on-site)
• Flexible working hours

Growth & development

• Customized training: access to Coursera to learn whatever you want, whenever you want
• Weekly language classes (English, Spanish & German)
• International Mentoring Sessions & Experience Days

Compensation & benefits

• Flexible compensation plan (health insurance, meal vouchers, childcare, transport)
• Telemedicine
• Life and accident insurance
• Social fund

Wellbeing & time off

• 26+ working days of vacation per year
• Free access to specialist services (medical, legal, wellness)
• 100% salary coverage during medical leave

And many more advantages of being part of T-Systems!

If you are looking for a new challenge, do not hesitate to send us your CV! Please send CV in English. Join our team!

T-Systems Iberia will only process the CVs of candidates who meet the requirements specified for each offer.