about 5 hours ago
Responsibilities
- Design, train, evaluate, and ship machine learning and deep learning models.
- Implement LLM-powered applications using major model providers.
- Build retrieval-augmented generation systems with chunking strategies and embeddings.
- Develop agentic workflows with tool use and orchestration.
- Apply prompt engineering and measure quality with evaluations.
- Maintain robust data and feature pipelines on Azure.
- Productionize models with sound MLOps practices.
- Use AI coding assistants effectively alongside manual coding.
- Manage source code in Git with a clean branching strategy.
- Build and maintain CI/CD pipelines for automated deployment.
- Deploy and operate services on Azure using containers and infrastructure-as-code.
- Instrument AI systems for quality, latency, and cost management.
- Partner with security and compliance for data privacy and model risk.
- Work with stakeholders to scope problems and set expectations.
- Mentor engineers on AI-assisted development practices.
- Conduct design and code reviews focusing on evaluation and safety.
Requirements
- Bachelor’s or Master’s degree in Computer Science, Machine Learning, or related field.
- 7+ years of professional software engineering experience.
- Strong proficiency in Python and core ML libraries.
- Hands-on experience deploying LLM-based applications.
- Practical experience with RAG and embeddings.
- Proficiency with AI coding tools and strong manual coding fundamentals.
- Solid command of Git and code review practices.
- Experience building and operating CI/CD pipelines.
- Experience deploying services on Azure and with containerization.
- Strong communication skills for explaining technical concepts.
- Ability to thrive in a remote, globally distributed team.