AI Lead Engineer - NLP, LLMs, conversational AI

Richard Wheeler Associates
Oxford
3 days ago
Create job alert

AI/ML Engineer - LLMs, NLP, Python, Chatbots, TechBio Scaleup

Circa £90k + Share Options, Private Health

Harwell Campus, Didcot, Oxfordshire - hybrid working


A one-off opportunity for a senior level AI engineer to join a seriously innovative TechBio scaleup (~30 people) developing novel cancer biologics.

We're seeking an AI Engineer to lead development of a conversational AI platform for structural biology and bioinformatics. This platform will democratize access to complex protein structure analysis through natural language interactions, integrating with existing databases and computational pipelines.

The AI Engineer will have core expertise in AI, LLMs, NLP frameworks - together with a software focus (Python, APIs, SQL, cloud) including proven experience of delivering products to clients.


The role: Platform Development

  • Design and implement LLM-powered conversational interfaces for bioinformatics workflows
  • Build function-calling systems that integrate Claude/OpenAI models with structural biology tools
  • Develop context-aware chat systems that maintain conversation history across sessions
  • Create modular, scalable architectures for bioinformatics data processing


Bioinformatics Integration

  • Integrate with protein structure databases (PDB, AlphaFold, custom TCR structures)
  • Build APIs connecting LLMs to molecular visualization tools (PyMOL, ChimeraX, NGLView)
  • Develop specialized functions for TCR-peptide-HLA interface analysis


Data Infrastructure

  • Design PostgreSQL schemas for storing structural and sequence data
  • Implement efficient data retrieval systems for large-scale protein datasets
  • Build real-time data pipelines for immune repertoire analysis
  • Optimize database performance for molecular structure queries


User Experience

  • Create intuitive chat interfaces using Streamlit or similar frameworks
  • Develop specialized prompting strategies for bioinformatics use cases
  • Build collaborative features for team-based structural analysis


Required Technical Skills:AI/ML Core

  • 3+ years hands-on experience (predominantly industry gained) with LLMs (GPT, BERT, T5, Claude) and NLP frameworks
  • Proficiency in prompt engineering, fine-tuning, and function-calling architectures
  • Experience with Hugging Face, OpenAI APIs, and transformer libraries
  • Understanding of model customization for domain-specific tasks
  • Experience with retrieval-augmented generation (RAG) systems
  • Knowledge of optimization techniques for reducing LLM latency and computational costs


Software Engineering & Architecture

  • Strong Python development skills (pandas, numpy, scikit-learn, PyTorch/TensorFlow)
  • Experience with web frameworks (Streamlit, FastAPI, or Flask) and frontend technologies
  • Database design and optimization (PostgreSQL preferred) for large protein datasets
  • API development and integration, especially for interfacing LLMs with external systems
  • Microservices architecture and containerization (Docker, Kubernetes)
  • Version control, CI/CD pipelines, and automated testing practices


Cloud & DevOps

  • Experience with cloud computing platforms (AWS, GCP, Azure) and big data technologies
  • Familiarity with DevOps practices and deployment of ML applications
  • Data preprocessing and pipeline automation for structured/unstructured biological data
  • Understanding of data security and compliance requirements for sensitive datasets


Bioinformatics & domain knowledge is strongly advantageous, though is NOT a pre-requisite.


Benefits:

In addition to a competitive salary, share options, private health, you will enjoy access to state-of-the-art computational resources and cutting-edge biological datasets; latest bioinformatics software, LLM frameworks, and protein modelling tools; the opportunity to work with novel therapeutic targets and protein engineering challenges.

This is an urgent requirement. Immediate availability or availability to start inside 4 weeks preferred.


JBRP1_UKTJ

Related Jobs

View all jobs

MLOps & AI Engineer Lead

MLOps & AI Engineer Lead

Machine Learning and AI Engineering Lead

AI Engineer

Lead Data Engineer

Lead Data Engineer - KPMG Curve

Get the latest insights and jobs direct. Sign up for our newsletter.

By subscribing you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Top 10 Mistakes Candidates Make When Applying for Machine-Learning Jobs—And How to Avoid Them

Landing a machine-learning job in the UK is competitive. Learn the 10 biggest mistakes applicants make—plus tested fixes, expert resources and live links that will help you secure your next ML role. Introduction From fintechs in London’s Square Mile to advanced-research hubs in Cambridge, demand for machine-learning talent is exploding. Job boards such as MachineLearningJobs.co.uk list new vacancies daily, and LinkedIn shows more than 10,000 open ML roles across the UK right now. Yet hiring managers still reject most CVs long before interview—often for avoidable errors. Below are the ten most common mistakes we see, each paired with a practical fix and a live resource link so you can dive deeper.

Top 10 Best UK Universities for Machine Learning Degrees (2025 Guide)

Explore ten UK universities that deliver world-class machine-learning degrees in 2025. Compare entry requirements, course content, research strength and industry links to find the programme that fits your goals. Machine learning (ML) has shifted from academic curiosity to the engine powering everything from personalised medicine to autonomous vehicles. UK universities have long been pioneers in the field, and their programmes now blend rigorous theory with hands-on practice on industrial-scale datasets. Below, we highlight ten institutions whose undergraduate or postgraduate pathways focus squarely on machine learning. League tables move each year, but these universities consistently excel in teaching, research and collaboration with industry.

How to Write a Winning Cover Letter for Machine Learning Jobs: Proven 4-Paragraph Structure

Learn how to craft the perfect cover letter for machine learning jobs with this proven 4-paragraph structure. Ideal for entry-level candidates, career switchers, and professionals looking to advance in the machine learning sector. When applying for a machine learning job, your cover letter is a vital part of your application. Machine learning is an exciting and rapidly evolving field, and your cover letter offers the chance to demonstrate your technical expertise, passion for AI, and your ability to apply machine learning techniques to solve real-world problems. Writing a cover letter for machine learning roles may feel intimidating, but by following a clear structure, you can showcase your strengths effectively. Whether you're just entering the field, transitioning from another role, or looking to advance your career in machine learning, this article will guide you through a proven four-paragraph structure. We’ll provide practical tips and sample lines to help you create a compelling cover letter that catches the attention of hiring managers in the machine learning job market.