Principal Python Backend Engineer

10h10 hours ago

Fidelity Investments

Durham, US · Full-time · $107,000 – $216,000

About this role

Bring a builder's mindset to Fidelity's Enterprise AI/ML Platform and help scale the next generation of high-performance, production-grade backend systems. You will work on the core platform that connects tools, agents, data, and models while designing clean service abstractions.

Build resilient processing pipelines and ship developer-friendly APIs, SDKs, and CLIs that make it simple to develop agents and information retrieval pipelines at enterprise scale. Turn rapid prototypes into well-engineered Python systems that remain maintainable and hardware-efficient.

We hire exceptional Python engineers who value clean code, fast learning, and high ownership. Deep AI/ML knowledge is not required upfront because domain context is learned on the job while engineering rigor cannot be taught.

Lead through code and knowledge sharing in a flat hierarchy where the best ideas win. Ask sharp questions, challenge complexity, and help replace bloated open-source frameworks with lean, well-engineered Python modules.

Requirements

7+ years of professional software engineering experience building and operating production-grade Python backend systems
Strong Python service engineering with sound OOP, clear interfaces, thorough tests, and obsession with readability and maintainability
Real-world performance tuning across services and data stores including concurrency, async I/O, queues, caching, and SQL/NoSQL indexing
Experience building event-driven systems and real-time pipelines for ingestion and inference
Mastery of debugging complex distributed behavior through reproducible experiments and evidence-driven conclusions
Comfort reading open-source code and producing simplified alternatives to minimize code legacy and cognitive load
Effective use of developer-assist tools to amplify output while keeping quality high and code bloat minimal

Responsibilities

Build the core AI/ML services running in Kubernetes and locally in Playground mode
Design clean abstractions over vector databases and multistep Search/Information Retrieval pipelines
Own automated real-time data ingestion for RAG including connectors, streaming pipelines, chunking, embedding strategies, and parallel processing
Ship developer-friendly APIs, SDKs, CLIs, and templates that simplify agent and tool development at enterprise scale
Instrument services with distributed tracing, retrieval quality metrics, performance metrics, and failure forensics
Turn rapid prototypes into resilient systems that are simple to use and scale in a hardware-efficient manner
Read and distill open-source frameworks, keeping what is valuable and replacing the bloated with lean Python modules