Senior Python Engineer - GenAI Platform

Ohio Farmers Insurance Company • Seville, OH • 2h ago

Job Summary
Westfield is seeking a Python Engineer to build and harden the runtimes, services, and tooling that power our enterprise GenAI platform. This is a backend/platform engineering role. As part of our GenAI team, you'll build APIs and agent operational frameworks, wire in observability and guardrails, enable systems to manage prompt/agent lifecycle, and achieve deploy on demand / release on demand practices at scale. You'll partner with DevSecOps, security, and application teams to make LLM-based agents and prompt workflows reliable, compliant, and fast. Finally, you'll aid in the operation of the platform, participating in monitoring and troubleshooting of the services you own.
Job Responsibilities
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.
Job Qualifications
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.
Preferred Qualifications
Experience with agent frameworks (e.g. LangGraph, Pydantic AI, or similar) and prompt/agent workflow orchestration.
Familiarity with prompt lifecycle management tools/patterns and automated LLM evals (quality/safety/regression).
Knowledge of vector search and caching patterns (e.g., pgvector, Redis, Elasticsearch) and async tasking (e.g. Celery, Redis Queue).
Infra-as-code (e.g. Terraform, Helm), container build/publish pipelines, and secure supply chain practices.
Exposure to operational monitoring/debugging tools (e.g. Dynatrace, Graylog) feature flag platforms, and secret management.
Understanding of DORA4 metrics with examples of improving lead time, deployment frequency, MTTR, and change failure rate.
Experience with uv for Python dependency/build management; familiarity with uvicorn (ASGI) is a plus.
Location

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