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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
* Use Infrastructure as Code (Terraform/CloudFormation) to provision scalable cloud infrastructure for training and real-time inference
* Ensure observability: monitoring, drift detection, hallucination mitigation, SLOs, and alerting for model and service health
* Serve models at scale: containerized, auto-scaling environments (e.g., Kubernetes) with low-latency inference
* Maintain data & model versioning with a central model registry, including lineage and rollback capabilities
* Deliver a live performance dashboard (e.g., intent accuracy, latency, error rates) and establish a retraining strategy
* Lead and foster innovation around frameworks and models; collaborate closely with product, engineering, and client stakeholders
Qualifications / Experience
* Relevant primary degree, ideally MSc or PhD
* Proven expertise in mathematics and classical ML algorithms, with 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 (Kubernetes) for low-latency serving
* Workflow automation: end-to-end ML lifecycle automation from data ingestion to model retraining and deployment
* Development of live performance dashboards displaying key metrics
* Management of centralized model registry with versioning and lineage
* Automated retraining workflows and documentation for periodic 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
* Integrity, stakeholder management, project management, agility, automation, data visualization, and analysis skills
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