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Principal Engineer - Python API Development

Fidelity Investments

Hoboken, NJ 07030

~ 16 min OnsiteEducation AssistanceHealth InsurancePaid Time OffRetirement Benefit

Bachelor's or Master's degree in Computer Science, Software Engineering, or a closely related engineering discipline
8+ years (typically 10+) building and operating production platforms and services at scale
Deep software engineering expertise in Python and distributed systems, with a track record of building production‑grade services, libraries, and internal platforms.
Linux fluency and scripting are required.
Cloud platform leadership (AWS) —hands‑on with S3, Lambda, Batch, Step Functions, EventBridge, CloudWatch, and SNS/SQS
Experience shaping platform patterns that other teams adopt
Experience enabling managed ML services (e.g., SageMaker) as part of broader platform capabilities
Infrastructure as Code (CloudFormation, Terraform/OpenTofu)
Platform reliability engineering (SLOs/error budgets, capacity planning, cost observability, incident response, and post‑mortems) for ML serving and data/feature pipelines
ML enablement in production: model packaging, deployment strategies (batch/online/streaming)
Inference routing
Traffic management
Performance tuning
Observability
Controls for responsible use—without a research or modeling focus
Cross‑org technical leadership: you mentor junior and senior engineers
Are a backbone of code review across repos
Routinely consider impacts on upstream/downstream systems when proposing changes
Set platform strategy and standards for ML packaging, deployment, serving, and observability
Driving consistent adoption across squads and business units
Partner with Data Scientists to package, scale, and operationalize models
Define the APIs, guardrails, and automation that take work from experimentation to reliable production
Enable secure, scalable access to traditional and generative models by collaborating with platform and application engineers to integrate through enterprise gateways and services
Advance model/data observability—tooling for data and feature drift detection
Prediction‑quality monitoring and uncertainty signals
Automated diagnostics/ explainability
Lead cross-platform incident response and post-mortems
Drive systemic fixes
Evolve standards to prevent recurrence—across applications and the platform
Uplevel engineering velocity by introducing reusable frameworks, paved paths, and CI/CD templates
That simplify integration, reduce toil, and improve reliability at scale
Reduce cost and complexity across the ML ecosystem through pragmatic technology choices, clear abstractions, and a long‑term platform roadmap