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.

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
• Implement CI/CD pipelines for ML/LLM: automated build/train/validate/deploy for chatbots and agent services
• Use Infrastructure as Code (Terraform/CloudFormation) to provision scalable cloud resources for training and inference
• Monitor model and service health with observability practices: drift detection, hallucination, SLOs, and alerting
• Serve models at scale using containerization, auto-scaling (e.g., Kubernetes), with low-latency inference
• Maintain data and model versioning with a central registry, including lineage and rollback capabilities
• Deliver a live performance dashboard (intent accuracy, latency, error rates) and establish retraining strategies
• Collaborate with product, engineering, and client stakeholders to foster innovation around frameworks and models

Qualifications / Experience:

• Relevant primary degree, ideally MSc or PhD
• Proven expertise in mathematics, classical ML algorithms, and deep knowledge of LLMs (prompting, fine-tuning, RAG/tool use, evaluation)
• Hands-on experience with AWS and Azure ML services (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 serving
• Workflow automation across the ML lifecycle: data ingestion, preprocessing, retraining, deployment
• Development of live performance dashboards displaying key model metrics
• Management of a centralized model registry with versioning and lineage
• Documented retraining strategies with automated workflows
• 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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