Principal Engineer Python API Development Fidelity
Overview
Lead design and delivery of enterprise AI/ML platform solutions. Own development of Python APIs and distributed systems. Drive platform strategy and standards. Shape ML packaging deployment observability and cost efficiency. Mentor engineers and lead cross‑team initiatives. Standalone what you will own at scale. Differentiate by leading technical vision and fostering reuse.
What You'll Do11
- 1Design Build and evolve reliable secure cost‑efficient platform capabilities from model packaging serving to observability and lifecycle management
- 2Investigate performance bottlenecks in web services analyze system metrics and optimize GPU utilization throughput and resource efficiency across ML workloads
- 3Own CI/CD workflows and infrastructure patterns across enterprise repositories with highest impact
- 4Partner with Data Scientists to package scale and operationalize models defining APIs guardrails and automation
- 5Enable secure scalable access to traditional and generative models via enterprise gateways providing usage visibility and cost insights
- 6Advance model data observability tooling for drift detection prediction quality monitoring and automated diagnostics
- 7Lead cross‑platform incident response and post‑mortems driving systemic fixes and preventive standards
- 8Uplevel engineering velocity by introducing reusable frameworks paved paths and CI/CD templates
- 9Reduce ML ecosystem cost and complexity through pragmatic technology choices and long‑term platform roadmap
- 10Collaborate with platform and application engineers to integrate models through enterprise gateways
- 11Set platform strategy and standards for ML packaging deployment serving and observability
Requirements11
- 1Bachelor’s or Master’s degree in Computer Science Software Engineering or related field
- 28+ years typically 10+ building and operating production platforms and services at scale
- 3Deep expertise in Python and distributed systems with production‑grade service library and internal platform experience
- 4Linux fluency scripting required Java or Groovy familiarity
- 5Familiarity with AWS cloud platform hands‑on S3 Lambda Batch Step Functions EventBridge CloudWatch SNS SQS and shaping platform patterns
- 6Experience enabling managed ML services such as SageMaker exposure to Azure or GCP beneficial
- 7DevOps and CI/CD at scale owning automated build test deploy standards Jenkins Git workflows Docker release governance multi‑environment promotion for ML workloads
- 8Infrastructure as Code using CloudFormation Terraform OpenTofu and platform reliability engineering SLOs error budgets capacity planning cost observability incident response and post‑mortems
- 9Cross‑org technical leadership mentoring engineers conducting code reviews and considering upstream downstream impacts
- 10Knowledge of GenAI Gateways or LiteLLM preferred
- 11Onsite working model in New York location
Salary Insight
$107 - $216k per year
Location
Required Skills
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