Principal Data Scientist

Social network you want to login/join with:

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 processes for chatbots and agent services
• Use Infrastructure as Code (Terraform/CloudFormation) to provision scalable cloud environments for training and real-time inference
• Monitor model and service health through observability, drift detection, hallucination checks, 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 model registry, including lineage and rollback capabilities
• Deliver a live performance dashboard tracking metrics like intent accuracy, latency, and error rates, along with a retraining strategy
• Lead and foster creativity around frameworks and models; collaborate closely with product, engineering, and client stakeholders

Qualifications / Experience

• Relevant primary degree, preferably 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 services for data/ML (e.g., Bedrock/SageMaker, Azure OpenAI/Azure ML)
• Strong engineering skills in Python, APIs, containers, Git; experience with 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, from data ingestion to model retraining and deployment
• Development of live performance dashboards displaying key metrics such as intent accuracy, response latency, and error rates
• Experience with centralized model registries, versioning, and lineage management
• Automated retraining workflows with proper documentation
• 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, Agile Methodologies, Automation, Data Visualization, and Analysis.

#J-18808-Ljbffr