Principal Software Development Engineer

Autodesk Autodesk · Enterprise · Mexico · Remote

Principal Backend / Data Engineer for Autodesk Info360 Operational Analytics team. Focus on designing, building, and scaling cloud-native backend services and APIs on AWS for high-frequency, large-scale time-series data. Key responsibilities include optimizing data pipelines, ETL/ELT processes, and building backend infrastructure to support AI/ML-powered features, model serving, feature pipelines, and predictive alerting. Role requires strong AWS, data pipeline, streaming, time-series data, and systems design experience, with familiarity in AI/ML infrastructure and observability.

What you'd actually do

  1. Design, build, and scale cloud-native backend services and APIs on AWS that handle high frequency sensor and time-series data.
  2. Build and optimize data pipelines and ETL/ELT processes for large-scale, near real-time ingestion and processing.
  3. Play a key role in delivering AI/ML-powered features by building the backend systems, data pipelines, and APIs that bring intelligent capabilities to production.
  4. Own features end-to-end from design through production including monitoring, reliability, and iteration.
  5. Build backend infrastructure that supports ML model serving, feature pipelines, and feedback loops for predictive alerting.

Skills

Required

  • 5+ years building and shipping large-scale backend systems in production
  • Hands-on experience with data pipelines and streaming architectures (Kafka, Kinesis or similar)
  • Experience with time-series data: storage, querying, or analytics at scale
  • Strong AWS experience (Lambda, API Gateway, Kinesis, DynamoDB)
  • Solid systems design skills across distributed and event-driven architectures
  • Familiarity with AI/ML infrastructure: building systems that support model serving or integration
  • Experience with Snowflake or comparable data warehouse
  • Strong observability and production debugging experience
  • Proven ability to collaborate and drive alignment across multiple teams

What the JD emphasized

  • high-frequency, large-scale data problems
  • AI/ML-powered features
  • backend systems, data pipelines, and APIs
  • ML model serving, feature pipelines, and feedback loops
  • large-scale backend systems
  • data pipelines and streaming architectures
  • time-series data
  • AI/ML infrastructure

Other signals

  • building backend systems for AI/ML features
  • building backend infrastructure for ML model serving
  • near real-time data processing for analytics and alerting