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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
1. Design & build client-specific GenAI/LLM virtual agents
2. Enable the orchestration, management, and execution of AI-powered interactions through purpose-built AI agents
3. Design, build, and maintain robust LLM-powered processing workflows
4. Develop cutting-edge testing suites related to bespoke LLM performance metrics
5. Implement CI/CD pipelines for ML/LLM: automated build, train, validate, deploy for chatbots and agent services
6. Use Infrastructure as Code (Terraform/CloudFormation) to provision scalable cloud infrastructure for training and real-time inference
7. Implement observability: monitoring, drift detection, hallucination management, SLOs, and alerting for model and service health
8. Serve at scale: containerized, auto-scaling environments (e.g., Kubernetes) with low-latency inference
9. Manage data & model versioning; maintain a central model registry with lineage and rollback capabilities
10. Deliver a live performance dashboard (intent accuracy, latency, error rates) and establish a retraining strategy
11. Collaborate closely with product, engineering, and client stakeholders to foster creativity 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 data/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 in scalable serving infrastructure: containerized, auto-scaling environments (e.g., Kubernetes) for low-latency serving
* Workflow automation across the ML lifecycle, from data ingestion to model retraining and deployment
* Experience with live performance dashboards displaying key model metrics
* Experience with centralized model registries and versioning
* Ability to document retraining strategies and automate 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
* Integrity, stakeholder management, project management, familiarity with Agile methodologies, automation skills, data visualization and analysis expertise
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