- Embed with clients to design and build AI solutions for real business problems.
- Take AI systems from early experimentation through deployment and production monitoring.
- Build production-grade GenAI applications using RAG architectures, agentic workflows, and fine-tuning.
- Establish evaluation baselines and metrics, then iterate on prompts, retrieval, models, and parameters.
- Manage AI system lifecycles, including experiment tracking, model versioning, observability, and deployment.
- Build automated pipelines for monitoring, evaluation, and CI/CD for traditional ML and LLMs.
- Design training and inference data pipelines that scale and run reliably in production.
- Deploy or work with AI solutions in cloud environments such as AWS, Azure, or GCP.
- Translate business challenges into technical roadmaps and lead projects from scoping to delivery.
- Explain architectural decisions to clients and collaborate with engineers to debug pipelines.
AWSPythonDatabricks+2 more