Head of AI Engineering
London, UK
Hybrid
Our client is an innovative startup partnering with leading organizations across the built environment. They are revolutionizing how industries leverage their data by delivering advanced machine learning and AI-powered insights. From international airports to large-scale rail projects and global real estate portfolios, their work spans diverse sectors, driving transformation through cutting-edge AI solutions.
This role focuses on optimizing retrieval-augmented generation (RAG) pipelines, fine-tuning large language models (LLMs), and delivering scalable ML solutions to generate actionable insights from complex enterprise data.
This role focuses on optimizing retrieval-augmented generation (RAG) pipelines, fine-tuning large language models (LLMs), and delivering scalable ML solutions to generate actionable insights from complex enterprise data.
Key Responsibilities:
Machine Learning Engineering:
Design and optimize production-grade RAG pipelines.
Develop sophisticated ML systems for information retrieval and generation.
Oversee model selection, fine-tuning, and evaluation processes.
Implement metrics and monitoring systems for ML model performance.
Optimize model inference and deployment pipelines for efficiency.
Technical Leadership:
Lead a team of ML engineers in developing and deploying AI solutions.
Design and implement ML infrastructure and scalable data pipelines.
Establish best practices for ML development, testing, and deployment.
Guide architectural decisions for AI/ML systems.
Implement ML operations for model monitoring and maintenance.
Product Development:
Collaborate with product teams to translate business needs into ML solutions.
Define technical specifications for new AI features.
Lead proof-of-concept development for novel ML capabilities.
Ensure optimal model performance for production environments.
Research & Innovation:
Explore and implement cutting-edge ML techniques, particularly in RAG and LLMs.
Evaluate emerging ML models and approaches for potential use.
Develop innovative solutions to complex information retrieval challenges.
Create frameworks for systematic model evaluation and improvement.
Requirements:
Advanced degree (Master’s/PhD) in Computer Science, Machine Learning, or a related field.
Over five years of ML engineering experience, focusing on production systems.
Expertise in building and optimizing RAG systems.
Extensive experience with LLMs and fine-tuning techniques.
Strong programming skills in Python and ML frameworks such as PyTorch or TensorFlow.
Proficiency with vector databases and embedding models.
Proven track record in deploying ML systems at scale.
Deep knowledge of ML operations and deployment strategies.
Expertise in natural language processing and information retrieval.
Experience with distributed systems and cloud computing.
Proficiency in developing ML infrastructure and pipelines.
Familiarity with ML monitoring and evaluation metrics.