Senior Backend Software Engineer (Python)
Role details
Job location
Tech stack
Job description
You will join the Predictive Intelligence engineering team within Anaplan, building the backend services that power the Syrup platform and Anaplan's forecasting and predictive solutions. The team owns the production services behind forecasting, scoring, and data processing, along with the pipelines and infrastructure that bring ML and AI capabilities to enterprise customers. As a senior backend engineer, you will design and build core services across this stack - writing clean, performant, well-tested Python that the rest of the team can build on. This role reports to the Director of Engineering for Predictive Intelligence and works closely with backend engineers, data scientists, and platform partners., * Design and implement scalable, fault-tolerant backend services and APIs as a core contributor across the Predictive Intelligence stack.
- Build and evolve the forecasting, scoring, and data processing services that deliver predictive insights, with a focus on performance, scalability, and cost efficiency.
- Write clean, well-tested, maintainable Python and establish patterns that make the codebase easier for the whole team to extend, operate, and reason about.
- Partner with data scientists and ML engineers to integrate models into production services and build the data and inference pipelines around them.
- Take part in on-call rotations and own the operational health of high-availability production services, including incident response and post-incident improvements.
- Lead design reviews and code reviews, raising the quality bar across the team and mentoring mid-level and junior engineers.
- Identify and drive cross-cutting platform improvements that benefit multiple services and teams.
Requirements
Do you have experience in Software deployment?, Do you have a Master's degree?, * Extensive professional software engineering experience building production backend services.
- Strong proficiency in Python, with a track record of writing performant, well-tested, maintainable production code.
- Hands-on experience operating containerized services on Kubernetes in at least one major cloud (AWS, GCP, or Azure).
- Experience with data warehousing or analytics technologies such as Snowflake, Iceberg, Trino, or Postgres.
- Working understanding of how ML and AI systems run in production, including the data and model pipelines that support them.
- Demonstrated ability to work autonomously, take ownership of meaningful systems, and deliver against ambiguous requirements.
- Track record of being on-call for production services and contributing to operational excellence.
- Bachelor's degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience.
Preferred Skills
- Hands-on MLOps experience, including model training pipelines, deployment workflows, and tooling such as MLflow.
- Experience productionizing data science or research code into stable, observable services.
- Familiarity with data orchestration tools (e.g., Prefect, Airflow, dbt) and modern data lake architectures.
- Experience with gradient-boosted tree models, neural networks, or optimization solvers in production.
- Exposure to LLM or agentic application patterns and AI service integration.
- Background in forecasting, demand planning, or retail/supply chain domains.