About the Role
You will experiment with and deploy AI copilot and autopilot systems to improve analyst productivity and eliminate manual decision loops. You will own the end to end system including API calls to disparate data sources, advanced prompt tuning, orchestration, metrics and monitoring. You will leverage diverse data sets including payment transactions, connected users and asset graphs to build transformer based ML models that improve detection tasks. You will collaborate with product, platform, engineering and operations to deploy production grade systems and monitor performance. You will use Python ML stack, LLMs, PyTorch, Snowflake, Airflow and cloud services (GCP and AWS) to get the job done. You will leverage agentic tools to accelerate research and development and documentation.
Requirements
- 8+ years of Machine Learning modeling experience; full stack ML experience is strongly preferred.
- A Masters or advanced degree in computer science data science operations research applied math stats physics or a related technical field.
- 3+ yrs experience with AI engineering, Large language models, and a background in traditional NLP techniques is a strong plus for this role.
- End to end experience of building and deploying ML/AI to production systems (batch and real time) that are performant at scale.
- Experience of independently owning, influencing and driving programs with multiple cross functional stakeholders that have significant business impact.
- Have a curious, growth-oriented mindset and the ability to think in first principles to identify creative solutions that demonstrate value.
Responsibilities
- Experiment and deploy AI copilot and autopilot systems at scale to improve analyst productivity and eliminate manual decision loops.
- Own the end to end system including API calls to disparate data sources advanced prompt tuning orchestration metrics evaluation productionization and monitoring.
- Leverage diverse data sets that include payment transactions connected users and asset graphs to build transformer based ML models to improve downstream detection tasks.
- Work cross functionally with product platform engineering and operational stakeholders to deploy production grade systems and monitor and tune ongoing performance.
- Use Python ML stack LLMs Pytorch Snowflake Airflow based tools data platform and cloud services (both GCP and AWS) to get the job done.
- Leverage agentic tools Claude Code Codex Openclaw to supercharge your research development and documentation work.
Benefits
- Remote work
- Medical insurance
- Flexible time off
- Retirement savings plans
- Modern family planning