Senior AI Engineer for AI Systems

We are building AI-powered features across our product, including intelligent assistants, document understanding, and automation workflows.

We are looking for an experienced AI Engineer who has **real hands-on experience building production systems with LLMs and RAG (Retrieval-Augmented Generation)**.

This is not a prompt engineering role.

We need someone who can design and build **end-to-end AI systems**.

--- • *What you will work on:** • Build RAG-based systems using company data (documents, cost catalogs, project data, etc.) • Integrate LLMs such as ChatGPT, Claude, and other models into production workflows • Design pipelines for: • Data ingestion (PDFs, documents, structured data) • Embedding and retrieval • Context management and ranking • Improve response quality, latency, and cost efficiency • Implement evaluation and feedback loops for AI outputs • Work on real use cases such as: • AI estimating assistant • Document parsing and insights • Smart search across project data • AI-powered recommendations

--- • *Requirements (must-have):** • Strong experience with RAG architectures (vector DBs, embeddings, retrieval strategies) • Hands-on experience with LLM APIs: • OpenAI (ChatGPT) • Anthropic (Claude) • Experience building production-grade AI systems (not prototypes only) • Solid backend skills (Node.js or Python) • Experience working with structured and unstructured data (PDFs, documents, APIs) • Understanding of token usage, latency, and cost optimization

--- • *Preferred experience:** • Experience with tools like: • Pinecone, Weaviate, or similar vector databases • LangChain, LlamaIndex (or custom pipelines) • Experience with document processing (OCR, parsing, chunking strategies) • Experience with multi-agent or workflow-based systems • Familiarity with AWS or similar cloud environments

--- • *What we care about:** • You have built real AI systems used by users • You think about quality, reliability, and cost • You can explain trade-offs (accuracy vs latency vs cost) • You don’t rely only on frameworks—you understand what’s happening under the hood

--- • *Project scope:** • Start with a defined project (2–4 weeks) • Opportunity for long-term collaboration if successful

--- • *To apply, please include:**

1. Example of a RAG system you built (architecture + what problem it solved)

2. How you approach: • Chunking • Retrieval • Context building

3. What you would improve in a typical “basic RAG setup”

4. Links to any relevant projects, repos, or demos

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We are looking for someone experienced who can move fast and build reliable systems.

If you’ve only experimented with LLMs or followed tutorials, this role is not a fit.