Full-Stack Engineer, AI, LLM Integration

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JetBridge AI

WebsiteLinkedInAll Job Openings

51 - 200 employees

Founded 2016

🤖 Artificial Intelligence

🤝 B2B

🎯 Recruiter

💰 $600k Seed Round - JetBridge on 2018-02

Artificial Intelligence • B2B • Recruitment

JetBridge AI is an AI transformation partner that provides senior AI/ML engineering teams, technical leadership, and AI infrastructure to VC-backed startups and enterprises. They build production-grade AI/ML products, recruit and place engineering talent, run pilot-to-rollout engagements, and offer consulting and research in areas like NLP, genomics, predictive analytics, and GPU-optimized model training. Clients span biotech, healthcare, finance, CX, manufacturing, logistics, and SaaS, and the company emphasizes delivering measurable ROI through embedded engineering teams and AI-driven systems.

📋 Description

• Clean up, refactor, and modularize a rapid-prototype React/Next.js frontend. • Design and implement a robust, production-ready backend architecture from scratch. • Move direct LLM API calls into a structured orchestration layer. • Build and optimize advanced prompt engineering workflows and image generation pipelines (specifically leveraging OpenAI's image generation models/DALL-E). • Set up data ingestion workflows to parse clinical study protocols. • Prepare and structure internal ad-performance training data for potential LLM fine-tuning. • Own the deployment, hosting, and monitoring strategy to transition the app from a fragile prototype to a stable, scalable MVP. • Ensure best practices for data handling (Note: the app processes study protocols and marketing data, not patient data/PHI, so full HIPAA/SOC 2 compliance is not required for this phase).

🎯 Requirements

• 4+ years of professional full-stack development experience (comfortably operating at a strong Mid to Senior level). • Deep expertise in React and Next.js. • Experience building production-grade backends (Node.js) and deploying apps to stable cloud environments. • Hands-on experience integrating LLMs, managing image generation pipelines, handling API rate limits, and implementing orchestration frameworks (e.g., LangChain, LlamaIndex, or custom structured outputs). • A "scrappy but disciplined" engineer who isn't afraid to dig into messy, auto-generated code and systematically refactor it without losing momentum.