## Essential Duties
- Design and execute machine learning experiments to evaluate emerging AI technologies and frameworks.
- Prototype and assess end-to-end AI solutions to inform product and platform strategy.
- Formulate hypotheses and conduct structured evaluations to compare technical approaches.
- Apply modern ML engineering practices to build and test scalable, modular proof-of-concept systems.
- Contribute to the definition of best practices for experimentation, evaluation, and technical decision-making.
- Synthesize and communicate experimental results to guide investment and adoption decisions.
- Translate new research ideas and tools into functional, decision-relevant demonstrations.
- Operate independently while contributing to a highly collaborative team environment.
- Communicate technical findings clearly and concisely to both technical and non-technical audiences.
## Minimum Qualifications
Bachelor's degree in computer science, data science, information technology, statistics, economics, or a related STEM field.3+ years of experience building machine learning-powered applications or tooling.Strong proficiency in Python and familiarity with modern ML / AI libraries (e.g., PyTorch, Hugging Face, OpenAI SDKs).Experience working with LLM orchestration or agent frameworks.Understanding of model tuning, prompt engineering, or retrieval-augmented generation (RAG) patterns.Experience developing and deploying applications in cloud environments (e.g., AWS, GCP, or Azure), including use of Docker and / or Kubernetes.Demonstrated ability to independently prototype, test, and iterate on technical ideas.Familiarity with ML evaluation techniques and structured experimentation workflows.Proficiency with version control systems, CI / CD practices, and ML observability tools.Show more
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Skills Required
Pytorch, Gcp, Docker, Version Control Systems, Azure, Kubernetes, Python, Aws