How we helped a SF based startup hire a specialized Full Stack Engineer

About Colare

Colare is a San Francisco-based startup reimagining how deep-tech companies hire. Their AI-powered platform helps hardware-first startups — from robotics to semiconductors — find and attract the specialized engineers they need to move fast. Colare was themselves an early TechKareer client, and after a successful Electrical Engineer placement, they returned with a new challenge.

This time, Colare needed to staff one of their portfolio companies — a well-funded SF-based startup building an AI-native data infrastructure product. The company had raised a $12M seed round from notable Bay Area investors and was racing to build out its engineering team before their next fundraise.

The Challenge

The client was looking for a specialized Full Stack Engineer who could operate across the entire stack — from designing RESTful and GraphQL APIs on the backend (Python/FastAPI, Node.js) to building polished, performant frontends in React and TypeScript. Critically, they needed someone with experience integrating large language models into production applications, including prompt engineering, streaming interfaces, and evaluation pipelines.

The combination of deep full-stack chops and hands-on LLM experience is rare. Most LLM engineers are primarily ML-focused, and most senior full-stack engineers haven't worked extensively with AI systems. The team had been searching for three months with no offer extended.

"Every candidate we saw was either a great engineer who'd never touched an LLM in production, or an ML person who couldn't build a frontend. We needed someone who could do both without hand-holding."

How TechKareer Helped

TechKareer's approach for this search combined targeted outbound recruiting with deep vetting to ensure every candidate presented was genuinely qualified — not just keyword-matched.

Key steps in our process:

• Technical profile mapping — we collaborated with the CTO to draft a detailed competency matrix covering backend architecture, frontend quality bar, and LLM integration depth
• AI-native talent network — we tapped our curated pool of engineers who had shipped production LLM features at companies like Cohere, Scale AI, and early-stage AI startups
• Portfolio-based screening — rather than relying on take-home tests, we asked candidates to walk us through live production systems they'd built, with specific focus on how they handled LLM latency, fallbacks, and observability
• Culture fit alignment — the client was a small team of 8 people with strong opinions about code quality and autonomy; we assessed for this explicitly in our screens

The Outcome

TechKareer surfaced the winning candidate in 21 days. She was a senior engineer who had previously led full-stack development at an AI infrastructure startup, and had personally shipped a real-time LLM document analysis feature used by 50,000+ enterprise users.

She joined as a Senior Full Stack Engineer and within her first month had shipped two major product features, refactored the frontend state management layer, and introduced a structured evaluation framework for their core LLM pipeline.

What Colare Said

"TechKareer found someone our team couldn't find in three months of searching. The candidate they placed is exactly what we were looking for — and the process was fast, transparent, and completely painless. It's become the default way we hire for our portfolio companies."