AI Research Engineer – LLMs, Agentic AI & Vision Equity

Rise Technical Recruitment Ltd; Greater London; On-site; GBP 80,000 - 120,000; Full time

Benefits offered by this job

Equity package

Bi-annual salary reviews

Annual equity refresh

Remote work abroad

Job summary

Rise Technical Recruitment Ltd is recruiting an AI Research Engineer for central London, office-based with a 4-5 day onsite pattern and a 4 weeks remote abroad option. The role will involve building and refining cutting-edge AI models, including LLMs and agentic systems, to support financial crime compliance initiatives.

You will work with a skilled team to shape AI strategy, evaluate models, and implement pipelines with strong emphasis on MLOps, benchmarking, and real-world problem solving.

Qualifications

• 3+ years' experience in AI Research, ML or AI Engineering.
• Strong experience with LLMs, prompt engineering and model fine-tuning.
• Experience building, testing, or deploying agentic AI systems.
• Knowledge of model evaluation, benchmarking and MLOps best practices.
• Passion for emerging AI technologies and solving real-world problems.

Responsibilities

• Build, evaluate and optimise LLM, agentic AI and computer vision systems.
• Fine-tune and train custom AI models for specialist compliance applications.
• Develop benchmarks, back testing frameworks and evaluation pipelines.
• Implement continuous improvement loops using human feedback and model iteration.
• Work closely with engineering and product teams to deliver AI-driven features.

Skills

AI Research

ML Engineering

LLMs

Prompt engineering

MLOps

Job description

Rise Technical Recruitment Ltd is recruiting an AI Research Engineer for central London, office-based with a 4-5 day onsite pattern and a 4 weeks remote abroad option. The role will involve building and refining cutting-edge AI models, including LLMs and agentic systems, to support financial crime compliance initiatives.

You will work with a skilled team to shape AI strategy, evaluate models, and implement pipelines with strong emphasis on MLOps, benchmarking, and real-world problem solving.