Principal Data Scientist

JR United Kingdom; Bedford; On-site; GBP 60,000 - 90,000; Full time

Job summary

A leading technology company in the UK seeks a talented individual to design and build Gen AI virtual agents. This role requires expertise in mathematics, classical ML algorithms, and significant experience with LLMs. Successful candidates will have strong engineering skills in Python and cloud technologies, and will be responsible for overseeing advanced AI interactions. A competitive salary and a collaborative work environment await the right candidate.

Qualifications

• Proven expertise in mathematics and classical ML algorithms, plus deep knowledge of LLMs.
• Hands-on experience with AWS and Azure services for data/ML.
• Strong engineering skills with Python, APIs, containers, and Git.
• Experience with scalable containerized serving infrastructure for chatbots.
• Ability to automate the machine learning lifecycle from data ingestion to deployment.

Responsibilities

• Design & build GenAI/LLM virtual agents for clients.
• Manage AI-powered interactions through dedicated AI agents.
• Develop testing suites related to LLM performance metrics.
• Automate CI/CD for ML/LLM, including deployment pipelines.
• Create centralized model registries and maintain model versioning.

Skills

Mathematics expertise

Classical ML algorithms

LLMs prompting

Fine-tuning

Python

APIs

Containers

CI/CD

Infrastructure as Code

AWS and Azure services

Education

Relevant primary level degree

MSc or PhD

Tools

Kubernetes

Terraform

CloudFormation

GitHub Actions

Azure DevOps

AWS Bedrock

Azure OpenAI

Job description

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You will be part of a team designing and building a Gen AI virtual agent to support customers and employees across multiple channels. You will build and run LLM-powered agentic experiences, owning the design, orchestration, MLOps, and continuous improvement.

• Design & build client-specific GenAI/LLM virtual agents
• Enable the orchestration, management, and execution of AI-powered interactions through purpose-built AI agents
• Design, build and maintain robust LLM powered processing workflows
• Develop cutting edge testing suites related to bespoke LLM performance metrics
• Develop bespoke testing suites and LLM performance metrics
• CI/CD for ML/LLM: automated build/train/validate/deploy pipelines for chatbots and agent services
• IaC - Infrastructure as Code, (Terraform/CloudFormation) to provision scalable cloud for training and real-time inference
• Observability: monitoring, drift detection, hallucination, SLOs, and alerting for model and service health
• Serving at scale: containerised, auto-scaling (e.g., Kubernetes) with low-latency inference
• Data & model versioning; maintain a central model registry with lineage and rollback
• Deliver a live performance dashboard (intent accuracy, latency, error rates) and a documented retraining strategy
• Lead and foster creativity around frameworks/models; collaborate closely with product, engineering, and client stakeholders

Qualifications / Experience

• Relevant primary level degree and ideally MSc or PhD
• Proven expertise in mathematics and classical ML algorithms, plus deep knowledge of LLMs (prompting, fine-tuning, RAG/tool use, evaluation)
• Hands-on with AWS and Azure services for data/ML (e.g., Bedrock/SageMaker, Azure OpenAI/Azure ML)
• Strong engineering: Python, APIs, containers, Git; CI/CD (GitHub Actions/Azure DevOps), IaC (Terraform/CloudFormation)
• Scalable Serving Infrastructure: A containerized, auto-scaling environment (e.g., using Kubernetes) to serve the chatbot model with low latency
• Workflow Automation: Automate the end-to-end machine learning lifecycle, from data ingestion and preprocessing to model retraining and deployment
• Live Performance Dashboard: A real-time dashboard displaying key model metrics such as intent accuracy, response latency, and error rates
• Centralized Model Registry: A versioned repository for all trained models, their performance metrics, and associated training data
• Documented Retraining Strategy: An automated workflow and documentation outlining the process for periodically retraining the model on new data
• Experience with Kubernetes, inference optimisation, caching, vector stores, and model registries
• Clear communication, stakeholder management, and a habit of writing crisp technical docs and runbooks

Personal Attributes

• Personal Integrity, Stakeholder Management, Project Management, Agile Methodologies, Automation, Data Visualisation and Analysis.