AI Engineer

Pecan is an automated AI-based predictive analytics platform. It simplifies and accelerates the process of building and deploying machine learning models in various business use-cases, such as life-time value, Churn, demand forecast and more. Pecan connects to the raw data and completely automates the data preparation, engineering and prepossessing phases, as well as the model training and evaluation lifecycle. It was acknowledged as one of Israel's 50 most promising startups two years in a row

Company Highlights:

• Series C company with over $117M raised to date. Tier-1 investors: Google Ventures (GV), Insight Partners, GGV, Dell Ventures, Mindset and S Capital.
• 50 employees
• Customers across CPG, retail, healthcare, mobile apps, fintech, insurance, and consumer services. Marquee customers include Johnson & Johnson,, and SciPlay..

Who You Are:

A problem solver at heart, you have a passion for excellence, you love to learn but know when it’s time to deliver and make ends meet. You aren’t threatened by a complex, dynamic and demanding environment. “There is no I in team”, is a motto you believe in deeply and you are always looking out for your peers. You know how to take ownership and drive projects to completion.

What We're Looking For:

• 5+ years of experience with Python.
• At least 1 year of experience developing LLM-based applications in production.
• Knowledge and experience with multi-agent systems and advanced AI architectures.
• Strong understanding of distributed systems, object-oriented programming and design patterns.
• LLM-related libraries and technologies such as LangChain, RAG, Agents, Langgraph etc.
• Hands-on experience with open source ML libraries like: Spark, NumPy, Pandas, Microservices architecture, cloud technologies, Docker/K8s.
• Ability to design and own a feature through all its phases.

Bonus:

• BSc./MSc. In CS or similar - an advantage
• Fine tuning of LLM models
• experience as a Machine Learning Engineer
• Hands-on experience designing, training, and deploying machine-learning models
• Building data pipelines using Apache Airflow