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- Tasks: Design and build innovative Gen AI virtual agents for customer support across multiple channels.
- Company: Join a forward-thinking tech company at the forefront of AI innovation.
- Benefits: Competitive salary, flexible working options, and opportunities for professional growth.
- Why this job: Be a key player in shaping the future of AI technology and customer experiences.
- Qualifications: Degree in a relevant field; expertise in ML algorithms and LLMs required.
- Other info: Collaborative environment with a focus on creativity and continuous improvement.
The predicted salary is between 72000 - 108000 £ per year.
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
- Craft context-aware, multi-channel self-service experiences
- 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
- Workflow automation across the ML lifecycle (data ingestion → retraining → deployment)
- 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.
Principal Data Scientist employer: ISx4
Contact Detail:
ISx4 Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Principal Data Scientist
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with potential colleagues on LinkedIn. You never know who might have the inside scoop on job openings or can put in a good word for you.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those related to LLMs and AI. This will give potential employers a taste of what you can do and set you apart from the crowd.
✨Tip Number 3
Prepare for interviews by practising common questions and scenarios related to data science and AI. Think about how you would tackle real-world problems and be ready to discuss your thought process.
✨Tip Number 4
Don't forget to apply through our website! We love seeing candidates who are genuinely interested in joining our team. Plus, it’s a great way to ensure your application gets the attention it deserves.
We think you need these skills to ace Principal Data Scientist
Some tips for your application 🫡
Show Your Passion for AI: When writing your application, let your enthusiasm for AI and data science shine through. We want to see how excited you are about building Gen AI virtual agents and improving customer experiences!
Tailor Your CV and Cover Letter: Make sure to customise your CV and cover letter to highlight your relevant experience with LLMs and MLOps. We love seeing how your skills align with our needs, so don’t hold back on the specifics!
Be Clear and Concise: Keep your application straightforward and to the point. We appreciate crisp communication, so avoid jargon overload and focus on what makes you a great fit for the Principal Data Scientist role.
Apply Through Our Website: Don’t forget to submit your application through our website! It’s the best way for us to receive your details and ensures you’re considered for the role. We can’t wait to hear from you!
How to prepare for a job interview at ISx4
✨Know Your Tech Inside Out
Make sure you’re well-versed in the technologies mentioned in the job description, especially around LLMs and MLOps. Brush up on your knowledge of AWS and Azure services, as well as CI/CD practices. Being able to discuss specific tools and frameworks will show that you’re not just familiar but also passionate about the tech.
✨Showcase Your Problem-Solving Skills
Prepare to discuss real-world scenarios where you've tackled complex problems using data science. Think about how you’ve designed workflows or improved processes in previous roles. Use the STAR method (Situation, Task, Action, Result) to structure your answers and highlight your impact.
✨Demonstrate Collaboration and Communication
Since this role involves working closely with product and engineering teams, be ready to share examples of how you’ve successfully collaborated in the past. Highlight your ability to communicate technical concepts clearly to non-technical stakeholders, as this is crucial for stakeholder management.
✨Prepare Questions That Matter
Interviews are a two-way street, so come prepared with insightful questions about the team’s current projects, challenges they face, and their vision for the Gen AI virtual agent. This shows your genuine interest in the role and helps you assess if it’s the right fit for you.
