Full Stack AI Developer

Overview

E2M Solutions works as a trusted white-label partner for digital agencies. We support agencies with consistent and reliable delivery through services such as website design, web development, ecommerce, SEO, AI SEO, PPC, AI automation, and content writing. Founded on strong business ethics, we are an equal opportunity organization powered by 300+ experienced professionals, partnering with 400+ digital agencies across the US, UK, Canada, Europe, and Australia. At E2M, we value ownership, consistency, and people who are committed to doing meaningful work and growing together. If you’re someone who dreams big and has the gumption to make them come true, E2M has a place for you.

Role Overview

We're looking for a product-minded Full Stack Developer who obsesses over user problems, not just code quality.

You'll own the full vertical—from the pixel to the prediction. You'll build interfaces users love in React, power them with blazing-fast FastAPI backends, and integrate AI that actually solves real problems (not just impressive demos). When off-the-shelf models fall short, you'll fine-tune them until they don't.

This isn't a "build what you're told" role. You'll be in the room shaping what we build, why we build it, and how AI can make it 10x better.

Key Responsibilities

• Product-First AI Development: Own features end-to-end—from user research insights to production deployment. You'll ask "what problem does this solve?" before "how do I build this?" Build AI-powered experiences that users actually adopt, not just technically impressive features that gather dust.
• High-Performance API & Intelligent Systems: Design robust, asynchronous APIs using FastAPI that handle complex AI workloads at scale. Optimize for the metrics that matter: latency, reliability, and cost-per-inference—because product economics matter as much as technical elegance.
• AI Model Lifecycle & Strategic Fine-tuning: Go beyond API wrappers. Use PyTorch, Hugging Face, and emerging 2026 frameworks to fine-tune LLMs, vision models, and multi-modal systems on proprietary data. Know when to buy vs. build, and make the ROI case for each approach.
• Unified Data Architecture for AI-Native Products: Design PostgreSQL schemas that scale, implement vector databases (pgvector, Pinecone) for RAG applications, and build the data foundations that make AI features actually work. Understand that data quality = AI quality.
• Cloud Architecture & Modern MLOps: Orchestrate production AI systems on AWS (EC2, S3, RDS, Lambda, SageMaker, Bedrock) with Docker and CI/CD. Implement observability, A/B testing infrastructure, and feature flags that let us ship fast and learn faster.
• UX-Obsessed AI Interfaces: Apply a designer's eye to AI features. Build chatbots that don't feel robotic, predictive dashboards that surface insights (not noise), and AI-assisted workflows that augment users rather than frustrate them. Identify and eliminate friction everywhere.
  
  

• 2–4 years professional React.js experience
• Mastery of Hooks, Context API, Server Components, and modern state management (Zustand, TanStack Query, Jotai)
• Build polished, accessible interfaces with Tailwind CSS or CSS-in-JS
• Experience with AI-specific UI patterns: streaming responses, progressive disclosure, confidence indicators
  
  

• Strong Python and FastAPI expertise
• Deep async/await understanding, Pydantic v2 validation, and scalable RESTful architecture
• Experience building real-time features (WebSockets, SSE) for AI streaming responses
• AI/ML Expertise
• Proven LLM integration experience (OpenAI, Anthropic, open-source models via Hugging Face)
• Hands-on model fine-tuning—LoRA, QLoRA, RLHF concepts
• Production RAG implementation with chunking strategies, embedding optimization, and retrieval tuning
• Familiarity with AI agents, function calling, and tool-use patterns
• Understanding of AI safety, hallucination mitigation, and responsible AI practices
  
  

• Expert PostgreSQL knowledge (relational design, indexing, query optimization)
• Vector database experience (pgvector, Pinecone, Weaviate)
• Understanding of embedding models and similarity search optimization
  
  

• Hands-on AWS experience: EC2, S3, RDS, Lambda, and AI services (SageMaker, Bedrock)
• IAM, security groups, VPC configuration
• Cost optimization mindset—AI inference is expensive; you care about unit economics
  
  

• Docker containerization and orchestration
• Linux/Ubuntu server management with Nginx and Uvicorn
• CI/CD pipelines with automated testing and deployment
  
  

• JWT, OAuth2, and modern auth patterns
• Understanding of AI-specific security: prompt injection, data leakage, PII handling
• Third-party API integration at scale
  
  

• SQLAlchemy / async database patterns
• Redis for caching, rate limiting, and task queues (Celery, ARQ)
• Experience with Supabase for rapid prototyping
• Background in UI/UX design tools (Figma, Adobe XD)
• Prior startup or product team experience