AI Engineer

Top 5 skills:

1. Production Experience with atleast 2 Agentic deployments
2. Deep understanding of harness engineering, grounding and evaluation
3. Strong python programming and pydyantic/ Langraph framework
4. LLM Ops, pipelines
5. Clear and strong verbal communication Required Qualifications Experience: 8+ years of progressive experience in Data Science, Machine Learning, or Applied AI research, with a minimum of 3 years in a leadership or principal role. Technical Depth: Expert proficiency in Python and ML frameworks (PyTorch, TensorFlow). Deep knowledge of statistical modeling, machine learning fundamentals, and distributed computing. Ownership Track Record: Demonstrated history of successfully taking ownership of complex, ambiguous AI projects and driving them from initial concept/POC to stable production release. Architecture: Strong understanding of Microservices architecture and experience designing cloud-native ML solutions (AWS, GCP, or Azure). Communication: Exceptional ability to distill complex technical and mathematical concepts into clear business implications for executive leadership.

Education

Master’s or Ph.D. in Computer Science, Applied Mathematics, Engineering, or a related quantitative field.