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AI & Machine Learning
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LLM & Generative AI
Everyone can call an API. The hard part is building something that works consistently, handles edge cases, and doesn't hallucinate in front of your customers. We find engineers who've shipped real GenAI products and dealt with the messy reality.
What We Deliver
Core Capabilities
We place engineers who go beyond the API wrapper -- people who understand RAG architecture, fine-tuning tradeoffs, prompt optimization, and the hard work of making LLM applications behave reliably in production.
RAG Systems
Engineers who build retrieval-augmented generation with proper chunking strategies, embedding selection, and vector store design -- not just a LangChain tutorial pasted together.
Fine-Tuning
Talent experienced with LoRA, QLoRA, and RLHF for adapting foundation models to domain-specific tasks where prompting alone hits a ceiling.
Agent Systems
Engineers who build multi-step agent workflows with tool use, planning, and memory management -- beyond chatbot wrappers.
Evaluation & Testing
People who approach LLM outputs systematically with evaluation frameworks, regression testing, and quality metrics. 'It looks good' isn't a deployment criteria.
Technology Stack
Tools & Technologies
Success Stories
Real-World Use Cases
Enterprise Knowledge Base
A GenAI engineer we placed built a RAG-powered Q&A system over 50,000+ internal documents for a client whose employees were losing hours to manual search every week.
Code Assistant
Talent we placed fine-tuned and deployed a coding assistant on a client's proprietary codebase, measurably reducing development time for their engineering team.
Customer Support Automation
An engineer we placed built an AI support agent that now handles the majority of a client's tier-1 tickets, freeing their team to focus on complex issues.
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