AL/ML Engineer

We’re Hiring: AI / ML Engineer GalaxiQ — (Remote-first) We’re building an AI-native marketing intelligence platform from the ground up. This is not a feature layer on top of existing software. We are building the core intelligence layer itself, combining Machine Learning + LLM systems into production-grade products. We’re looking for an engineer who can operate at the intersection of: Machine Learning + LLM Systems + Production Backend Engineering What You’ll Build You will work on core AI systems including: • ML-driven ranking, classification, and prediction systems for marketing intelligence • LLM orchestration pipelines (multi-step reasoning, tool use, structured outputs) • Production-grade Python services powering AI + ML workflows • RAG systems, embedding pipelines, and semantic retrieval architectures • Model evaluation frameworks (accuracy, drift, hallucination control, latency-cost tradeoffs) • API-first AI systems designed for scale • Data pipelines feeding ML + LLM systems This role sits at the core of product and architecture. What We’re Looking For We are specifically looking for someone with real Machine Learning experience , not just LLM usage. Must have: • Strong Python engineering ability (production-level, not notebooks only) • Solid foundation in Machine Learning (supervised / unsupervised / ranking / classification models) • Hands-on experience building ML systems in production • Understanding of prompt engineering and LLM workflows • Deep understanding of LLMs (OpenAI / Anthropic / open-source models) • Experience building end-to-end AI systems (data → model → API → product) • Strong backend/API development skills • Understanding of embeddings, vector search, and retrieval systems (RAG) We are building systems that run in production, at scale, with real users and real constraints. Bonus (Strong Signals) • Experience with ML model deployment (not just training) • Familiarity with LangChain / LlamaIndex / similar orchestration frameworks • Experience with model evaluation / benchmarking pipelines • Exposure to marketing tech / recommendation systems • Startup experience or 0→1 product building • Cloud + containerised deployment (AWS / GCP / Docker) Why GalaxiQ We are early-stage, fast-moving, and architecture-heavy. You will: • Own core ML + AI systems end-to-end • Shape product intelligence, not just implement features • Work without legacy constraints • Move fast in a high-ownership environment We’re building an AI marketing suite that replaces fragmented marketing workflows with autonomous, intelligent systems. Send: • CV / LinkedIn • GitHub / ML projects / system design work • Anything showing real ML + production AI experience • or DM on LinkedIn