Build Python services & SDKs that expose LLM/agent capabilities to internal teams; operate them on Kubernetes/OpenShift.
Support agent runtimes & workflows by implementing and managing operational workflows around AI technologies (e.g. LangGraph, OpenAI Agent SDK, MCP, others).
Develop a cohesive enterprise platform for GenAI use cases that run the gamut from core insurance workflows to back office assistants.
Own platform reliability, scalability, performance, and cost.
Observability: add tracing/metrics/logging via our enterprise tools and monitoring infrastructure to create actionable dashboards/alerts.
Security & governance: keep linters and code scan reports clean; enforce RBAC, audit trails, data-access policies, PII controls, and prompt-injection defenses.
CI/CD at scale: help own Azure DevOps YAML pipelines (pipeline-as-code) to enable deploy on demand; use feature flags and other techniques for release on demand.
Testing culture: drive a culture of fast unit tests, contract tests, and performance tests; keep coverage meaningful and PR checks green.
Dependency & packaging hygiene: manage Python envs and builds with uv and containerization.
Docs & enablement: produce runbooks, reference implementations, and developer guides; mentor teams on how to use the platform.

4+ years of software engineering experience, including building backend services in Python (e.g. FastAPI, Flask), with strong API design and production operations experience.
Demonstrated understanding of common software patterns and when to apply them.
Demonstrated experience running microservices and/or containerized deployments in production.
Hands-on experience with production logging, metrics, and tracing.
Experience satisfying automated code quality checks (e.g. SonarQube, Snyk).
Solid understanding of Git workflows, code reviews, feature flags, and trunk-based development practices that enable deploy on demand / release on demand.
Comfortable with platform governance concepts like audit logging, RBAC, data privacy boundaries, and change control in business-critical environments.
Comfort with AI Coding Assistants like GitHub Copilot or Claude Code in day-to-day work.
Strong testing discipline (e.g. pyunit/unittest, pytest), mocking, and CI gating.

Applicants must be currently authorized to work in the United States on a full-time basis without employer sponsorship.

Senior Python Engineer – GenAI Platform

Key skills