Job Description

What Youll Own Full Stack Systems: Architect and build end-to-end applications using Flask, FastAPI, Node.js, React (or Next.js), and Tailwind. AI Integrations: Build and optimize pipelines involving LLMs (OpenAI, Groq, LLaMA), Whisper, TTS, embeddings, RAG, LangChain, LangGraph, and vector DBs like Pinecone/Milvus. Cloud Infrastructure: Deploy, monitor, and scale systems on AWS/GCP using EC2, S3, IAM, Lambda, Kafka, and ClickHouse. Real-time Systems: Design asynchronous workflows (Kafka, Celery, WebSockets) for voice-based agents, event tracking, or search indexing. System Orchestration: Set up scalable infra with autoscaling groups, Docker, and Kubernetes (PoC ready, if not full prod). Growth-Ready Features: Implement in-app nudges, tracking with Amplitude, AB testing, and funnel optimization. Tech Stack Youll Work With: Backend & Infrastructure Languages/Frameworks: Python (Flask, FastAPI), Node.js Databases: PostgreSQL, Redis, ClickHouse Infra: Kafka, Docker, Kubernetes, GitHub Actions, Cloudflare Cloud: AWS (EC2, S3, RDS), GCP Frontend React / Next.js, TailwindCSS, Zustand, Shadcn/UI WebGL, Three.js for 3D rendering AI/ML & Computer Vision LangChain, LangGraph, HuggingFace, OpenAI, Groq Whisper (ASR), Eleven Labs (TTS) Diffusion Models, StyleGAN, Stable Diffusion GANs, MediaPipe, ARKit/ARCore Computer Vision: Face tracking, real-time try-on, pose estimation Virtual Try-On: Face/body detection, cloth/hairstyle try-ons APIs Stripe, VAPI, Algolia, OpenAI, Amplitude Vector DB & Search Pinecone, Milvus (Zilliz), custom vector search pipelines Other Vibe Coding culture, prompt engineering, system-level optimization Must-Haves: 1+ years of experience building production-grade full-stack systems Fluency in Python and JS/TS (Node.js, React) shipping independently without handholding Deep understanding of LLM pipelines, embeddings, vector search, and retrieval-augmented generation (RAG) Experience with AR frameworks (ARKit, ARCore), 3D rendering (Three.js), and real-time computer vision (MediaPipe) Strong grasp of modern AI model architectures: Diffusion Models, GANs, AI Agent Hands-on with system debugging, performance profiling, infra cost optimization Comfort with ambiguity fast iteration, shipping prototypes, breaking things to learn faster Bonus if youve built agentic apps, AI workflows, or virtual try-ons