Principal Data Scientist

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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
• Implement CI/CD pipelines for ML/LLM: automated build, train, validate, and deploy for chatbots and agent services
• Utilize Infrastructure as Code (Terraform/CloudFormation) to provision scalable cloud environments for training and real-time inference
• Implement observability: monitoring, drift detection, hallucination management, SLOs, and alerting for model and service health
• Serve models at scale: containerized, auto-scaling environments (e.g., Kubernetes) with low-latency inference
• Manage data & model versioning; maintain a central model registry with lineage and rollback capabilities
• Deliver a live performance dashboard (e.g., intent accuracy, latency, error rates) and establish a retraining strategy
• Lead and foster creativity around frameworks/models; collaborate closely with product, engineering, and client stakeholders

Qualifications / Experience

• Relevant primary level degree, 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 experience with AWS and Azure services for data/ML (e.g., Bedrock/SageMaker, Azure OpenAI/Azure ML)
• Strong engineering skills: Python, APIs, containers, Git; CI/CD (GitHub Actions/Azure DevOps), IaC (Terraform/CloudFormation)
• Experience with scalable serving infrastructure: containerized, auto-scaling environments (e.g., Kubernetes) for low-latency model serving
• Workflow automation across the machine learning lifecycle: data ingestion, preprocessing, retraining, deployment
• Development of live performance dashboards displaying key metrics such as intent accuracy, latency, and error rates
• Management of a centralized model registry with versioning, lineage, and performance metrics
• Automated retraining workflows and documentation for model updates
• Experience with Kubernetes, inference optimization, caching, vector stores, and model registries
• Excellent communication skills, stakeholder management, and ability to produce clear technical documentation and runbooks

Personal Attributes

• Personal integrity, stakeholder management, project management, familiarity with Agile methodologies, automation skills, data visualization and analysis capabilities.

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