Principal Software Engineer – AI Infrastructure

Required Qualifications Core Experience 10+ years of experience building and operating production-scale distributed systems. Proven experience designing and developing scalable backend platforms and services. Strong understanding of software architecture, system design, and cloud-native development. Experience translating ambiguous business requirements into technical solutions and execution plans. Data & AI Platform Experience Experience building features for ML inference pipelines. Strong understanding of: Data normalization Feature stores Model serving infrastructure Data processing frameworks AI/ML platform architecture Experience enabling machine learning workloads in production environments. AI-Assisted Development Demonstrated proficiency with modern AI development tools, including: GitHub Copilot LLM-powered engineering workflows Agentic coding assistants AI-assisted software development practices Ability to effectively leverage AI tools to improve code quality, engineering productivity, and development speed. Greenfield Product Development Proven track record of taking systems and platforms from 0 to 1. Comfortable working with undefined requirements and rapidly evolving priorities. Strong ownership mindset with the ability to drive initiatives independently. Architecture & Leadership Ability to lead architectural discussions and influence technical direction. Experience communicating complex technical concepts across engineering and business stakeholders. Strong decision-making skills around system design, scalability, reliability, and maintainability. Technical Skills Backend Development Java Spring Boot REST APIs Microservices Architecture Event-Driven Systems Cloud & Infrastructure AWS Compute, Storage, Networking IAM Cloud-native Architecture AI / Data Platforms Databricks ML Model Registry Feature Pipelines Data Platform Engineering Programming Languages Java Python JavaScript / TypeScript Development Tools GitHub Copilot AI-Assisted Development Tools Modern CI/CD Practices Ideal Candidate Profile You will be successful in this role if you: Have built AI-enabled platforms rather than simply consuming AI technologies. Have experience supporting ML inference and production AI systems. Use AI tools extensively to improve engineering efficiency and quality. Thrive in startup-like environments with high ownership and rapid execution. Enjoy solving complex technical challenges with limited direction. Collaborate effectively across Engineering, Product, and Data Science teams. Influence through technical excellence, architecture, and hands-on execution.

Lead the design and development of scalable AI infrastructure and production-scale distributed systems. Drive the transition of platforms from initial concept to production while influencing technical direction and architectural decisions.

Requires over 10 years of experience in distributed systems and backend platforms with a strong focus on ML inference and AI platform architecture. Proficiency in Java, Python, and AWS, along with experience using AI-assisted development tools, is essential.

• CI/CDPreferred
• Amazon Web ServicesPreferred
• JavaPreferred
• Spring BootPreferred
• PythonPreferred

Identified from the job description. Confirm important requirements above.

• Distributed Systems
• Backend Development
• ML Inference Pipelines
• System Design
• Cloud-native Architecture
• Java
• Python
• Spring Boot
• AWS
• Databricks
• Microservices
• Event-Driven Systems
• AI-Assisted Development
• Feature Stores
• Model Serving Infrastructure
• CI/CD

• Software
• Technology
• Engineering
• Data & Analytics
• Finance & Accounting

Minimum experience

10+ years

Apply by

Sep 19, 2026

Posting language

English

Working hours

40 hours per week

Seniority

Mid-Senior level

Application method

Direct apply is available