AI/ML Research Engineer

Role Overview

Help us push the boundaries of what AI can do in business automation. You will research, prototype, and productionize AI/ML solutions, designing RAG pipelines and fine-tuning LLMs for real-world, domain-specific tasks. This role suits someone who enjoys moving quickly from research to a deployable, reliable product feature.

Key Responsibilities

• Research, prototype, and productionize AI/ML solutions for business automation use cases.
• Design and evaluate RAG (retrieval-augmented generation) pipelines and prompt strategies.
• Fine-tune and evaluate LLMs for domain-specific tasks.
• Collaborate with backend engineers to deploy models as reliable, scalable services.
• Stay current with the AI research landscape and bring in applicable techniques.
• Build evaluation frameworks to measure model quality and regressions.
• Document experiments, findings, and model behaviour for the wider team.

Requirements

• 2+ years of experience in applied machine learning or AI engineering.
• Strong Python skills and experience with ML/AI frameworks (PyTorch, LangChain, etc.).
• Practical experience with RAG pipelines, embeddings, and vector search.
• Understanding of LLM fine-tuning, evaluation, and prompt engineering.
• Ability to translate ambiguous business problems into technical experiments.
• Comfortable working with large datasets and data preprocessing pipelines.
• Strong analytical and communication skills to present findings clearly.

Preferred Qualifications

• Published research, competitions, or open-source contributions in ML/AI.
• Experience with vector databases (Pinecone, Weaviate, pgvector).
• Familiarity with MLOps tooling for model deployment and monitoring.
• Experience with cloud GPU infrastructure and model serving.
• Exposure to multi-agent or tool-using LLM systems.

Compensation & Benefits

• Competitive, experience-based compensation benchmarked to your skill level.
• 5-day work week with a generous annual leave policy, including WFH days.
• Flexible, hybrid-friendly working hours built around deep-focus time.
• Annual learning budget for courses, certifications, and conferences.
• Latest MacBook/hardware and the tools you need to do your best work.
• Comprehensive health insurance for you and your family.
• Performance-linked bonuses and long-term equity/ownership potential.