Senior Technical Developer (Full-Stack & AI/ML Pipelines)

Job Overview
Type of Work: Contract (Long-term)
Hours Per Week: 25 to 40 hours/week
Timezone: Must have overlapping hours with EST/CST
Salary/Wage: $7-$16

We are looking for a highly execution-focused Senior Technical Developer to work directly with the founder and own the technical and systems layer of an enterprise project controls platform. Our platform replaces messy spreadsheets with a production-live system serving major multi-million dollar capital construction programs.

This is a hands-on implementation role for an expert-level TypeScript and React developer who wants to build real-world ML/AI infrastructure (fine-tuned LLMs, voice interactive pipelines)—not just wrap basic APIs. There is zero corporate theater or management layers here ; you will build, test, and ship clean code directly to production.

Main Responsibilities / Day-to-Day
- Full-Stack Feature Ownership: Autonomously decompose feature briefs, design structural technical architectures, and ship clean Pull Requests (PRs).
- AI & Machine Learning Infrastructure: Build and maintain robust ML pipelines, covering training data preparation, fine-tuning scripts, and voice pipelines (STT/TTS).
- Database & Backend Engineering: Write complex, raw PostgreSQL database schemas, advanced queries (CTEs, window functions), and handle data migrations with zero reliance on an ORM.
System Optimization: Develop, scale, and optimize high-throughput model inference steps and resolve backend production bugs.

Essential Qualifications / Skills (Must-Haves)
- Strict TypeScript/JavaScript Mastery: 3+ years of production experience (generics, type inference, discriminated unions).
- Production React & Next.js Literacy: Clear ownership over shipping live web apps using Next.js App Router (server vs. client components, middleware, route handlers).
- Raw SQL Fluency: Expert with raw PostgreSQL; you do not use or need an ORM.
- Python for Data/ML: Python proficiency explicitly tied to data preparation, training, and evaluation for machine learning pipelines.
- Strict Git Workflow Discipline: Maintaining clean commits and structural code review habits.
-Meticulous QA Mindset: You independently research unfamiliar stacks, test your own edge cases, and ensure work is right the first time.

Preferred Skills (Nice-to-Haves)
- Hands-on PyTorch and Hugging Face model fine-tuning (PEFT/LORA, Transformers library).
- Voice/speech ML engineering background (STT, TTS, or end-to-end voice pipeline tuning).
- GPU infrastructure management capabilities (self-hosted GPU servers, CUDA, resource monitoring).
- Front-end data visualization expertise (Tailwind CSS, Radix unstyled primitives, complex dashboard charting).
- DevOps exposure (Docker containerization, multi-stage builds, and automated CI/CD pipelines).


Ramp-Up Milestones
Day 30: Local environment running smoothly, 5–8 production PRs successfully merged, and full data tracing verified from the database to the UI.
Day 60: Confidently writing raw SQL migrations, handling active production bugs, and launching initial LLM fine-tuning experiments.
Day 90: Decomposing broad feature briefs into standalone technical tasks and actively contributing to voice AI and ML infrastructure pipelines.