Description:
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