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- Tasks: Join a team to design and build cutting-edge Gen AI virtual agents for diverse customer interactions.
- Company: Be part of an innovative company at the forefront of AI technology and customer support solutions.
- Benefits: Enjoy flexible working options, competitive salary, and opportunities for professional growth.
- Why this job: Work on exciting projects that shape the future of AI while collaborating with creative minds.
- Qualifications: A relevant degree, strong ML knowledge, and hands-on experience with AWS or Azure are essential.
- Other info: Ideal for those passionate about AI and eager to make a real impact in tech.
The predicted salary is between 57600 - 84000 £ 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
Familiarise yourself with the latest advancements in Gen AI and LLMs. Understanding the nuances of prompting, fine-tuning, and evaluation will give you a significant edge during discussions with our team.
✨Tip Number 2
Showcase your hands-on experience with AWS and Azure services. Being able to discuss specific projects where you've utilised tools like SageMaker or Azure ML will demonstrate your practical knowledge and readiness for the role.
✨Tip Number 3
Prepare to discuss your experience with CI/CD pipelines and Infrastructure as Code. Highlighting your familiarity with tools like GitHub Actions or Terraform will show that you can contribute to our automated workflows effectively.
✨Tip Number 4
Emphasise your ability to communicate complex technical concepts clearly. As this role involves collaboration with various stakeholders, showcasing your communication skills will be crucial in demonstrating your fit for the team.
We think you need these skills to ace Principal Data Scientist
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in data science, particularly with LLMs and AI technologies. Emphasise your hands-on experience with AWS, Azure, and any specific projects that align with the job description.
Craft a Compelling Cover Letter: In your cover letter, express your passion for AI and data science. Discuss how your background aligns with the responsibilities of designing and building Gen AI virtual agents, and mention any specific achievements that demonstrate your expertise.
Showcase Technical Skills: Clearly outline your technical skills related to Python, CI/CD, and containerisation. Provide examples of how you've used these skills in past projects, especially in relation to scalable serving infrastructure and workflow automation.
Highlight Collaboration Experience: Since the role involves collaboration with product, engineering, and client stakeholders, include examples of successful teamwork in your application. Mention any experience you have in stakeholder management and project management to showcase your interpersonal skills.
How to prepare for a job interview at ISx4
✨Showcase Your Technical Expertise
Be prepared to discuss your hands-on experience with LLMs and classical ML algorithms. Highlight specific projects where you've implemented these technologies, especially in relation to AWS or Azure services.
✨Demonstrate Problem-Solving Skills
Expect to face scenario-based questions that assess your ability to design and build AI-powered interactions. Think through your approach to problem-solving and be ready to explain your thought process clearly.
✨Communicate Clearly and Effectively
Since the role involves stakeholder management, practice articulating complex technical concepts in a straightforward manner. Prepare to discuss how you would document processes and communicate with non-technical team members.
✨Prepare for Collaborative Discussions
Collaboration is key in this role. Be ready to share examples of how you've worked closely with product and engineering teams. Emphasise your ability to foster creativity and drive innovation within a team setting.
