AI Tooling Engineer

Zyte

. Build the platform with the Principal Engineer in Tooling: new capabilities every week, from design to production.

Posted 10/21/2026full-timeRemote • IndiaMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and shipping production software using Python and relational databases, with a strong focus on integrating APIs and cloud infrastructure. Proficient in AI tooling initiatives, including evaluation, optimization, and security best practices.

Highest-signal resume keywords

Python DevelopmentAPI IntegrationCloud Infrastructure (Google Cloud/AWS)AI Tooling EvaluationMCP Protocols

ATS Keywords

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Hard Skills

Production Software DevelopmentRelational DatabasesLangGraphLLM APIsSQLDjangoPostgresReactNext.jsVersion Control (GitHub/Bitbucket)

Soft Skills

Excellent Written CommunicationExcellent Spoken Communication

Tools & Technologies

MCP ServersLangfuseData WarehousesAI Coding AgentsSemantic LayersKnowledge Graphs

Industry Keywords

Cloud ToolsInternal ToolsWeb ScrapingSecurity and PermissionsDistributed Team Experience

Tech Stack

Tools & technologies

AWSCloudDjangoJavaScriptNext.jsPostgresPythonReactSQL

About the role

Key responsibilities & impact

Build the platform with the Principal Engineer in Tooling: new capabilities every week, from design to production.
Connect new internal systems and SaaS tools to the platform, including APIs, data, shared models, and access permissions.
Build agents and their tools, including MCP servers and the context needed for high-quality answers.
Own evaluation sets, benchmarks, tracing, cost per answer, and issue resolution.
Turn proofs of concept into secure, production-ready internal cloud tools.
Use AI coding agents with specifications, tests, and reviews to maintain code quality.
Contribute to company-wide AI tooling initiatives, including tool evaluation and standardization, cost and usage tracking, and standards for AI-built internal applications.
Help other teams build on the platform.
Engage platform users across the company to identify priorities and explain delivered work to technical and non-technical audiences.

Requirements

What you’ll need

3+ years building and shipping production software, mostly in Python, with relational databases.
Has built and shipped with LangGraph at least one agent that real people use: tool calling, retrieval and embeddings, prompt and context design.
Hands-on experience with LLM APIs from the main providers, cost and token optimisation.
Hands-on with MCP or similar tool protocols, as a server and as a client.
Evaluates AI systems properly: test sets, LLM-as-judge, regressions when a prompt or a model changes; tracing and observability with Langfuse or similar.
Integrates APIs and data: SaaS APIs, SQL, data warehouses.
Experience with cloud infrastructure (Google Cloud and/or AWS): containers, managed databases, identity and access, CI/CD.
Uses AI coding agents every day and knows how to keep their output in check.
A sense for security and permissions: identity, least privilege, people see only what they are allowed to see.
Comfortable with version control (GitHub/Bitbucket).
Excellent written and spoken English, technical and non-technical.
Familiarity with web data or web scraping.
Django and Postgres, including vector search with pgvector.
Enough frontend to build and change a chat interface (React, Next.js).
Has built internal tools that colleagues adopted.
Semantic layers or knowledge graphs.
Memory for agents, multi-agent setups.
Previous remote/distributed team experience.

Benefits

Comp & perks

Become part of a self-motivated, progressive, multi-cultural team.
Freedom and flexibility to work remotely.
Work with cutting-edge, open-source technologies and tools — and shape the AI tooling the rest of the company relies on.