AI Engineer

- Developing and evolving AI benchmarking and experimentation platforms to support model evaluation
- Designing and executing end-to-end benchmarking workflows to generate actionable insights for client use cases
- Building scalable evaluation frameworks and metrics by integrating hands-on engineering with academic research
- Maintaining and enhancing experimentation infrastructure to support robust and production-grade engineering
- Producing clear insights and supporting technical demonstrations and deep dive sessions for clients
- Utilizing Python for asynchronous programming, multithreading, and writing maintainable code
- Building automated testing pipelines to assess model performance effectively
- Deploying machine learning workloads to cloud platforms such as Azure, AWS, or GCP
- Implementing CI/CD processes and containerization using Docker or Podman
- Applying knowledge of large language models (LLMs) and utilizing APIs for LLMs like Azure and OpenAI
- Conducting market analysis and data analytics to inform AI model selection and technical strategy
- Collaborating with forensic technology teams to address fraud, investigations, and regulatory enforcement challenges