Principal Data Scientist

About ISx4

ISx4 is a dynamic and innovative team, we harness the power of artificial intelligence and cloud computing to drive real-world impact. We work at the forefront of AI technology, leveraging leading cloud platforms to build scalable, intelligent solutions. We provide Insight through Strategy, Solutions, Services & Support (Sx4). Our industry experience help derive competitive advantage for our clients, taking them on the journey from Hindsight, through Insight to Foresight.

We create, deliver and manage Analytics and AI solutions across all sectors including finance, government, life sciences, private sector and sport. Our services are increasingly in demand as Analytics and AI services become a core competency in every area of the business. ISx4 are formal certified partners with Microsoft, AWS, GCP, SAS, IBM and Databricks and have customers & staff located globally. These strategic partnerships allow us to remain vendor-neutral and focused on our core competency area of analytics services delivery. ISx4 have an ambitious hiring plan for the next few years with new hires at all levels to support the strategic growth of the firm.

The Role

Our team will be 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.

Job Responsibilities

• 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 Skills

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

Desirable Skills

• AI / MLOps awareness to include safety/guardrails, red-teaming, and evaluation frameworks for LLMs
• Retrieval design (chunking, embeddings), observability stacks
• Familiarity with enterprise security, privacy, and compliance in production AI systems