Full-Stack GenAI Developer

Role Mission

We are seeking an elite Full-Stack GenAI Developer to design, build, and scale our next-generation agentic workflows. You are not just a coder; you are a systems thinker capable of bridging the gap between local LLM orchestration (Ollama/Jan.ai) and enterprise-grade cloud environments (GCP/AWS). Your mission is to build autonomous, self-healing systems that leverage the Model Context Protocol (MCP) and advanced graph-based reasoning to solve complex business challenges.

Technical Stack & Requirements

1. AI & Orchestration (The Core)

Agentic Frameworks: Expert-level proficiency in LangChain and LangGraph for stateful, multi-agent orchestration.Model Context Protocol (MCP): Hands-on experience implementing MCP to standardize tool-calling and context sharing across diverse models.Local & Open Ecosystems: Deep familiarity with local inference and development platforms including Ollama, Dify, Jan.ai, and Gorilla.Model Interoperability: Experience working with OpenClaw (or similar self-hosted agent runtimes) to execute system-level tasks via natural language.

2. Full-Stack Engineering & Cloud

Languages: Mastery of Python (FastAPI/Flask) for backend logic and AI integration.Cloud Infrastructure: Competency in Google Cloud Platform (GCP) and AWS, specifically regarding Vertex AI, Lambda/Cloud Functions, and secure IAM/VPC configurations.Workspace Integration: Ability to build custom apps and automations within Google Workspace (Apps Script, Gmail/Drive APIs).

3. Data Extraction & RPA

Web Scraping: Advanced experience with Playwright, Selenium, or BeautifulSoup, including bypass techniques for sophisticated anti-bot measures.RPA (Robotic Process Automation): Building robust, browser-based or desktop-based automations that mimic human interaction where APIs are unavailable.API Architect: Designing and consuming complex REST/GraphQL APIs with a focus on rate-limiting, error handling, and data integrity.

Key Responsibilities

Architect Agentic Workflows: Design state machines using LangGraph that can reason, loop, and self-correct during complex multi-step tasks.Hardware-to-Cloud Bridging: Manage the lifecycle of models from local testing (Ollama) to production deployment on ---------- tomated Data Pipelines: Build resilient scrapers and RPA bots that feed real-time data into vector databases or agentic memory.System Integration: Use MCP to connect AI agents to internal tools, databases, and third-party SaaS platforms seamlessly.

Qualifications

5+ years of Full-Stack development experience.Proven track record of deploying production-grade AI agents (not just "wrappers").Deep understanding of RAG (Retrieval-Augmented Generation) and vector database optimization.A "Builder" mindset: You contribute to open-source or have a portfolio of complex "side projects" that demonstrate your curiosity.

The Selection Process: Technical "Gauntlet"

Our vetting process involves a live coding challenge where you will be asked to:

Connect an AI agent to a live API using ---------- sign a brief LangGraph state machine to handle a multi-step research ---------- bug a failing web scraper in a containerized environment.

Work Environment

This is a fully virtual position offering flexibility to work from anywhere while maintaining collaborative relationships with tea ---------- mbers and clients across various time zones.
Application Process

To be considered for this position, please visit: ----------

This position requires candidates to successfully complete our comprehensive evaluation process, which includes technical assessments and multiple interview stages to ensure the best fit for both the role and our organizational culture.