AI Data Engineer

Compensation: No equity

Specification-Driven Extraction Engineering:

• Design and maintain declarative extraction specifications—using Pydantic models, JSON schemas, or domain-specific languages—that describe exactly which fields to capture, their types, and validation rules.
• Implement pipelines that translate these specifications into executable extraction plans, leveraging both classical (Scrapy, Playwright) and AI-augmented (LLM-based semantic parsing) backends.
• Build reusable specification libraries for recurring data types (product prices, tariff codes, regulatory texts) to accelerate onboarding of new sources.

Autonomous & Self-Healing Systems:

• Deploy self-healing spiders that automatically detect website layout changes and repair themselves using Model Context Protocol (MCP) servers (e.g., Scrapy MCP Server, Playwright MCP).
• Integrate semantic extraction (Scrapy-LLM, custom LLM pipelines) to eliminate selector brittleness—spiders rely on field descriptions, not fragile XPaths.
• Hands-on experience building AI agents and orchestration systems.
• Orchestrate complex, multi-step browsing workflows with agentic frameworks (BMAD/TEA, AutoGPT-like agents) that reason about page state, adapt to anti-bot measures, and correct their own behaviour in real time.

Platform Thinking & Reusability:

• Move beyond one-off scrapers: build a component-based extraction platform where selectors, login handlers, and pagination logic are shared, versioned, and tested.
• Implement monitoring, alerting, and automatic rollback for failed extraction runs.
• Champion ethical crawling by design—rate limiting, robots.txt respect, and compliance with GDPR/CCPA are built into the specification layer, not retrofitted.

Collaboration & Continuous Innovation:

• Partner with data scientists and domain experts to refine extraction specifications for complex, unstructured domains (e.g., legal texts, tariff classifications).
• Evaluate and pilot emerging tools to push automation coverage beyond 90%.
• Document and evangelise specification-driven best practices across the engineering organisation.

Qualification:

• Bachelor’s degree in Computer Science
• 3+ years of experience in web scraping or data extraction

Required Skills:

• Proficiency with Python
• Experience with specification-Driven Extraction
• Experience with LangChain, LangGraph, LlamaIndex, AutoGen
• Hands‑on use of Scrapy‑LLM, Scrapy MCP Server, or similar systems that decouple field definitions from page structure
• Familiarity with frameworks that give LLMs browser control (Playwright + MCP, BMAD/TEA) to handle complex, non‑deterministic crawling tasks.
• Design and implement autonomous data extraction agents that can make decisions about source selection, retry logic, and parsing strategies
• Classical Scraping Fundamentals
• Data Validation & Storage – Ability to define validation rules within specifications and land clean data into SQL/NoSQL databases or data lake
• Basic API integration and authentication flows.
• HTTP, DOM, XPath, CSS.

Nice to Haves:

• Contributions to open-source scraping or AI-automation projects.
• Contributions to open-source scraping or AI-automation projects.
• Familiarity with data privacy engineering (GDPR, CCPA) baked into specification design.
• DevOps light – Docker, CI/CD for testing extraction specifications.

Mindset & Approach (Non-Negotiable):

• Strong belief that the future of scraping is declarative, not imperative.
• Candidate rather write a schema that says “extract the price” than debug an XPath when a website redesigns.
• Looking to shift from “code that scrapes” to “systems that understand extraction”