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# Senior Backend Engineer

- **Company:** FLEXAI LLC
- **Location:** San Jose, CA, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Microsoft Azure, Software as a Service, Cloud Computing, Cloud Engineering, Databases, Continuous Integration, Data Systems, DevOps, Distributed Data Store, Distributed Systems, Amazon DynamoDB, Job Scheduling, Python (Programming Language), PostgreSQL, NoSQL, Platform as a Service (PAAS), Software Architecture, Redis, Tensorflow, Prometheus, Azure Machine Learning, Software Engineering, SQL Databases, AI Infrastructure, Data Logging, Pytorch, Grafana, Multi-Cloud, Caching, Backend, Event Driven Architecture, Containerization, AI Platforms, Kubernetes, Deployment Automation, Cassandra, Apache Kafka, Data Management, Machine Learning Operations, Stream Processing, Grpc, Docker, Golang, Programming Languages, Crud, Microservices
- **Published:** November 3, 2026
- **Apply:** https://www.careerbuilder.com/job-details/senior-backend-engineer-san-jose-ca--3ac09b0f-25b4-49a3-b6b1-fc56475e2723

## About the Role

Artificial Intelligence (AI), Distributed Computing, Platform as a Service (PaaS), Software Engineering, Cloud Computing, Go Programming Language (Golang), Production Systems, Software as a Service (SaaS), Application Programming Interface (API), Systems Administration/Management, As a Senior Backend Engineer, you'll drive backend architecture, scale platform services, and build high-performance infrastructure components that power AI workloads in production environments - influencing how the platform evolves from Beta to enterprise-grade deployment. Expect high ownership and technical autonomy in a research-driven, deep-tech environment - not SaaS CRUD apps., * Languages: Golang (Primary), Python (Secondary)

 * Infrastructure: Kubernetes, Docker, Cloud (AWS/GCP/Azure)
 * Architecture: Microservices, gRPC, Event-driven systems
 * Data: SQL + NoSQL databases, caching, streaming systems
 * Observability: Prometheus, Grafana, OpenTelemetry (or similar), * 5+ years of Backend or Infrastructure Engineering experience
 * Expert-level proficiency in Golang (must-have, heavy hands-on)
 * Strong experience building production-grade distributed systems
 * Proven track record on infrastructure platforms, PaaS, or deep-tech systems, * Deep understanding of cloud-native architectures and containerized environments
 * Strong experience with Kubernetes, Docker, and cluster orchestration
 * Familiarity with compute scheduling, resource management, or platform runtimes is a strong plus

Databases & Data Systems:

 * Experience with distributed databases (PostgreSQL, Cassandra, DynamoDB, etc.)
 * Strong understanding of caching, queues, and streaming systems (Redis, Kafka, etc.)

AI / Platform Exposure (Highly Preferred):

 * Experience on AI/ML platforms, model infrastructure, or data platforms
 * Familiarity with ML pipelines, inference systems, or GPU-backed workloads
 * Exposure to PyTorch, TensorFlow infrastructure, or model serving systems is a plus, * Infra-first backend engineers (not just API developers)
 * Background in AI infra, cloud platforms, developer platforms, or deep-tech systems
 * Strong systems thinkers who enjoy low-level performance, scalability, and architecture challenges
 * Startup-minded builders comfortable in ambiguous, high-ownership environments

## Description

* Architect and develop high-performance Golang services for FlexAI's AI PaaS and infrastructure platform
 * Build internal APIs powering model deployment, job scheduling, and compute lifecycle management
 * Develop components interfacing with GPU/compute infrastructure and AI runtimes, * Collaborate with AI/ML and Runtime teams to integrate systems with training pipelines, inference infrastructure, experimentation workflows, and dataset/artifact management
 * Enable orchestration across cloud and on-prem environments
 * Build abstractions that simplify AI infrastructure consumption

Cloud-Native & Platform Engineering:

 * Design cloud-native, Kubernetes-native services
 * Work with DevOps/SRE on CI/CD, deployment automation, and scalability
 * Contribute to architecture decisions for multi-region, multi-cloud infrastructure
 * Improve monitoring, logging, and diagnostics

Technical Leadership:

 * Lead architecture reviews and set engineering standards
 * Mentor engineers and guide complex problem-solving
 * Drive long-term roadmap for backend infrastructure and AI platform capabilities
 * Partner with Product, Runtime, and Infra leadership to translate requirements into scalable systems