Role

The Senior AI Engineer builds and deploys production-grade AI agents that power ACQ Vantage. This role exists to turn AI capability into real, usable systems that drive business outcomes, not experiments or prototypes.

You will work inside a lean Technology team to design, build, and ship agentic workflows that interact with internal tools, data systems, and user-facing products. You are responsible for taking ideas from concept to production, with a focus on reliability, speed, and practical value.

This is a hands-on engineering role. You are writing code daily, iterating quickly, and working directly with modern AI tooling. You are expected to understand how LLMs behave in production, not just how they work in theory.

Responsibilities

• Design, build, and deploy production-grade AI agents and end-to-end agentic workflows that solve real business problems across ACQ Vantage

• Integrate LLMs with internal systems, APIs, and data sources, ensuring reliability, performance, and clean abstractions

• Collaborate with product and engineering teams to prioritize, ship, and iterate on AI features quickly

• Own and improve RAG pipelines across multiple Pinecone namespaces, including chunking strategy, embedding model selection, hybrid retrieval, and reranking

• Build and maintain an evaluation framework, including golden datasets, automated quality scoring, retrieval metrics, latency benchmarks, and regression detection

• Optimize model routing and tiering to improve unit economics while maintaining output quality

• Instrument the AI layer for observability, including cost-per-request, token usage, quality signals, and anomaly detection

Requirements

• 7+ years shipping production software systems (distributed backends, APIs, deployment pipelines, monitoring)

• 2+ years building production RAG systems using vector databases (Pinecone, Qdrant, FAISS, or Weaviate), including embedding strategies, index management, and retrieval tuning

• Built and deployed AI agents or multi-step LLM workflows in production, including tool use, orchestration, and system integrations

• Built or contributed to an evaluation framework for an LLM-based product (retrieval quality measurement, regression detection, model-switching decisions based on data)

• Reduced LLM API costs in production through model routing, caching, token management, or architectural improvements