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# Sr. Software Engineer, AI Platform & Infrastructure

- **Company:** Okta, Inc.
- **Location:** San Francisco, CA, United States
- **Experience:** Expert
- **Salary:** $165,000.0 - $226,600.0
- **Contract:** Permanent contract
- **Skills:** LangGraph Framework, Query Performance, Application Programming Interfaces (APIs), Amazon Web Services, Amazon S3, BigQuery, Software as a Service, Databases, Customer Data Management, Data Infrastructure, Data Systems, Distributed Systems, Identity and Access Management, Python (Programming Language), PostgreSQL, OAuth, OpenID, OpenAI, Standard Sql, Security Assertion Markup Language (SAML), Software Deployment, Enterprise Software Applications, Okta, ReactJS, Database Performance, AgentCore, Backend, Agentic-AI, Fastapi, Servicebus, Pgvector, AI Platforms, Amazon Bedrock, Deployment Automation, Front End Software Development, Vertica, Claude, Functional Programming, Cloudwatch, Api Gateway, Software Coding, Amazon Simple Queue Service (SQS), Agent SDK, Servicenow
- **Published:** October 23, 2026
- **Apply:** https://diversityjobs.com/career/18740966/Sr-Software-Engineer-Ai-Platform-Infrastructure-California-San-Francisco

## About the Role

* Experience building and operating production backend or distributed systems, with strong coding skills in Python or a comparable language.
 * Hands-on AWS experience with APIs, queues, containers, networking, IAM and databases.
 * Strong SQL and data modeling skills. Experience diagnosing database performance and asynchronous system failures.
 * Experience with infrastructure as code, deployment automation, observability and production incident response.
 * Sound judgment about customer data, tenant isolation, credentials and safe changes to external systems.

Scope by level

Senior: Own a backend or platform component, including deployment, tests and production operation.

Staff: Lead a domain such as execution, tenant security, data infrastructure or reliability across teams.

Principal: Set shared backend and platform contracts, resolve system tradeoffs and implement foundational services.
Our stack

Python FastAPI, AWS Step Functions, Amazon Bedrock AgentCore, Lambda, SQS, EventBridge, API Gateway and containers. Data and security use RDS PostgreSQL, pgvector, S3 Object Lock, KMS, Secrets Manager, CloudWatch and CloudTrail. The frontend uses React and AWS Amplify.

OpenAI and Anthropic model integrations are planned. We are also adding an agent harness and dedicated analytics. ClickHouse is a hiring focus for analytics, and BigQuery or equivalent experience is relevant. The harness and analytics choices remain open.
And extra credit for

 * ClickHouse, BigQuery or comparable analytics systems, event ingestion and usage metering.
 * Multi-tenant SaaS, OAuth, OIDC, SAML, SCIM or enterprise connector development.
 * Deploying agent frameworks, including Claude Agent SDK, Codex SDK, Strands Agents, Deep Agents or LangGraph. Prior identity experience is optional.

## Description

You will build the backend and AWS platform that keep these workers running around the clock. The team owns services, connectors, workflow execution, data and production reliability. You will write Python services and infrastructure code that let workers make approved changes and preserve evidence.
About You

Reliable builder: You design for failure and prove that recovery works.

Hands-on engineer: You write production services and improve the tools engineers use every day.

Strong owner: You follow changes through deployment, monitoring and production support.
What you will be doing

 * Build Python FastAPI services, data models and reusable connectors for Okta, AWS, ServiceNow and enterprise applications.
 * Implement durable execution with Step Functions, SQS and EventBridge. Handle approval waits, rate limits, duplicate events and safe retries.
 * Manage PostgreSQL schemas, indexes, query performance and pgvector retrieval. Enforce tenant isolation across APIs, storage and credentials.
 * Build model API access for OpenAI, Anthropic and supported AWS paths, including credentials, quotas, timeouts and cost controls.
 * Add an analytics pipeline for worker events, traces, tokens, findings and verified outcomes. Evaluate ClickHouse and comparable systems such as BigQuery.
 * Create reliable usage records and reconciliation. Prevent retries, duplicate findings and incomplete work from being counted as new outcomes.
 * Build deployment pipelines, infrastructure as code, observability and safe release controls across development, staging and production.
 * Implement scoped write services, kill switches, audit retention and backup recovery. Test failures and verify that sensitive changes actually took effect.