Requirements
- Bachelor's, master's, or doctoral degree in Computer Science, Data Science, or a closely related discipline.
- Minimum of six years of direct industry experience demonstrating leadership and end-to-end project delivery.
- Solid grasp of core machine learning principles, including data workflow design and model validation techniques.
- Expertise in Python and common data science libraries such as NumPy, pandas, and scikit-learn; knowledge of JVM-based languages is beneficial.
- Understanding of large language model fundamentals, prompt engineering strategies, and agent-based architectural patterns.
- Proven background in building and managing distributed systems that process high-volume datasets.
- Excellent verbal and written communication abilities, along with a drive to influence the evolution of AI technologies.
Nice to Have
- Experience managing and guiding teams of software or machine learning engineers.
- Track record of developing and releasing AI agents or LLM-powered systems into live environments.
- Knowledge of current frameworks for agentic AI, including LangGraph, LangChain, or CrewAI.
- Hands-on experience with machine learning platforms and tools such as PyTorch, MLflow, Airflow, Docker, and AWS.
- Exposure to LLM operations, including system efficiency tuning, monitoring, latency analysis, and cost tracking.
Compensation
Competitive salary and benefits package commensurate with experience.
Work Arrangement
Hybrid or remote work options available based on role and location.
Team
Part of a forward-thinking AI and machine learning team focused on cutting-edge data and language technologies.
Required (7)
- Bachelor's, master's, or doctoral degree in Computer Science, Data Science, or a closely related discipline.
- Minimum of six years of direct industry experience demonstrating leadership and end-to-end project delivery.
- Solid grasp of core machine learning principles, including data workflow design and model validation techniques.
- Expertise in Python and common data science libraries such as NumPy, pandas, and scikit-learn; knowledge of JVM-based languages is beneficial.
- Understanding of large language model fundamentals, prompt engineering strategies, and agent-based architectural patterns.
- Proven background in building and managing distributed systems that process high-volume datasets.
- Excellent verbal and written communication abilities, along with a drive to influence the evolution of AI technologies.
Preferred (5)
- Experience managing and guiding teams of software or machine learning engineers.
- Track record of developing and releasing AI agents or LLM-powered systems into live environments.
- Knowledge of current frameworks for agentic AI, including LangGraph, LangChain, or CrewAI.
- Hands-on experience with machine learning platforms and tools such as PyTorch, MLflow, Airflow, Docker, and AWS.
- Exposure to LLM operations, including system efficiency tuning, monitoring, latency analysis, and cost tracking.
Visa sponsorship may be available for qualified candidates.
