Model and Intelligence Engineer

We are looking for a highly skilled Model and Intelligence Engineer to own the end-to-end development and continuous evolution of our advanced Deep Research Agent. The ideal candidate will be an expert in building robust RAG pipelines (from chunking to re-ranking), optimizing context compression for complex, multi-hop queries, and establishing rigorous evaluation harnesses for model benchmarking. Beyond core engineering, this role requires a strategic thinker who can drive agent self-improvement loops, lead prompt and fine-tuning strategies, and translate frontier AI research into production-ready features for our platform's intelligence roadmap.

Responsibilities

Own the end-to-end design, development, and continuous improvement of the Deep Research Agent; Design and maintain the RAG pipeline: chunking strategy, embedding models, retrieval, and re-ranking; Implement and optimize context compression to reduce overhead on long-horizon, multi-hop queries; Build and operate the model evaluation harness: benchmark design, regression tracking, and A/B testing; Lead the agent self-improvement loop: prompt proposal pipeline and benchmark-gated merge governance; Track frontier model research and assess production applicability for the platform intelligence roadmap; Advise on fine-tuning, prompt optimization, and model selection strategies across model generations

Requirements

Deep expertise in LLMs: transformer architecture, fine-tuning (LoRA/QLoRA), RLHF, and alignment techniques; RAG system design: vector databases (Pinecone, Weaviate, pgvector), embedding models, hybrid search strategies; ML experimentation tooling: MLflow, Weights & Biases, Vertex AI Experiments, or equivalent platforms; Python ML stack: PyTorch or JAX, HuggingFace Transformers, LangChain or equivalent orchestration libraries; Statistical evaluation methods: benchmark design, significance testing, and evaluation dataset curation; Context compression and KV cache optimisation techniques, quantisation basics (GPTQ, AWQ)