Senior Software Engineer, AI Platform

Role Overview

Team Context

What You Will Do

• Build product features end to end on internal AI tools in TypeScript or Python, from design through production.
• Turn repeated product patterns into shared services and libraries for model access and spend, identity and permissions, retrieval, tool servers, and LLM call controls.
• Build MCP tool servers that expose company data to agents and chat tools with end-user permissions enforced.
• Help introduce relationship-based access control across products for database queries and vector search.
• Standardize model access through the LLM gateway so model choice, cost, and usage are set in one place.
• Spread safety patterns such as prompt-injection handling, kill switches, sandboxed execution, and locked-down data access.
• Make shared pieces easy to adopt with clear APIs, versioning, templates, and documentation.
• Set the standard in code review, including for AI-assisted code, and mentor engineers.
• Own incidents on what you build and work with DevOps engineers on deployment and operation.

What We Are Looking For

• Six or more years building and running production software, including at least one shared service, library, or internal API other teams depended on.
• Strong TypeScript and Node.js, and strong Python.
• Shipped LLM features to production: retrieval over embeddings, tool calling, structured output, and output quality checks.
• Designed APIs or libraries other engineers build on, with attention to versioning, compatibility, safe defaults, and documentation.
• Built multi-tenant or permission-sensitive systems and knows where to enforce access.
• Solid system design: service boundaries, data modeling, failure handling, idempotency, and cost.
• Production experience on AWS, with infrastructure as code in Terraform or similar.
• Writes tests as part of the work and treats CI checks as gates.
• Clear written and spoken English, able to explain technical tradeoffs to stakeholders.
• Uses AI coding tools and verifies their output.

The Stack

• Languages: TypeScript and Node.js, Python 3.11.
• Services: Express, FastAPI, Next.js.
• AI: OpenAI models and embeddings, LangChain, access control with SpiceDB (designed, being introduced).
• Model Context Protocol (MCP) SDK and an internal LLM gateway built on LiteLLM over Amazon Bedrock.
• Cloud and Delivery: AWS (EC2, ECS Fargate, Lambda, App Runner, S3, CloudFront), Terraform, GitLab and GitHub.
• Data: PostgreSQL (Amazon RDS and Aurora), BigQuery, Amazon OpenSearch Serverless for vector search.
• Identity and Access: Okta, OAuth 2.0 with PKCE, relationship-based access control with SpiceDB (designed, being introduced).
• Work Tracking: Jira, Confluence.

Nice to Have

• Relationship-based access control in practice, with SpiceDB, OpenFGA, or similar.
• An LLM gateway such as LiteLLM or Amazon Bedrock, and evaluation or tracing tools such as Braintrust, Langfuse, or LangSmith.
• An agent orchestration framework such as LangGraph.
• A shared platform that served several teams or departments rather than one product.
• Experience in games, or Arabic.

Benefits

• Mobile Phone Allowance.
• Health Insurance.
• Performance Bonus.
• Relocation support where applicable.