AI ML Engineer
Job Summary
We are seeking an AI/ML Engineer with 1-5 years of experience to join our team for the Krunchbox Reimagined project. The role focuses on building production-grade AI agents, RAG systems, and predictive models using retail data. You will be responsible for end-to-end development, from prototype to production deployment.
Responsibilities
- Build AI agents and chatbots
- Develop RAG pipelines for LLMs
- Create predictive ML models for retail analytics
- Perform feature engineering and model evaluation
Required Skills
- Python
- Machine Learning (Regression, Time-series)
- LLMs and Prompt Engineering
- RAG and Vector databases
- Backend / API Development
Job Details
What You’ll Do:
- Build AI agents (“Krunchy”) that generate Insights, Reports, and Recommendations.
- Develop RAG pipelines combining LLMs with structured data (POS, inventory, product data).
- Create chat-based experiences for customer analytics.
- Machine Learning Modeling (Core): build and improve models for Demand forecasting, Stockout risk, Lost sales estimation, and Anomaly detection.
- Perform feature engineering on messy retail datasets.
- Model evaluation and iteration.
- Help take models from prototype to production.
Tech Stack:
- Python (FastAPI preferred), LLM APIs (OpenAI, Anthropic), LangChain / LlamaIndex (or similar), Vector databases, ClickHouse / modern data stack, AWS / cloud infrastructure.
Requirements:
- 2–4 years of experience in ML, data science, or backend engineering.
- Strong Python skills.
- Experience building APIs or backend systems.
- Experience with machine learning modeling (e.g., regression, time-series, classification, or similar).
- Exposure to LLMs, chatbots, or prompt engineering.
- Comfortable working with messy datasets.
Nice to Have:
- RAG or vector search experience.
- Time-series forecasting (Prophet, XGBoost, etc.).
- Retail / supply chain data experience.
- MLOps or production ML exposure.
Benefits:
- Competitive compensation package.
- Comprehensive health and benefits coverage.
- A predominantly in-person, collaborative work environment to encourage fast iteration and real-time problem solving.
- Opportunity to scale and lead a global SaaS platform.
- Partially remote: Work from home some days a week.
- Computer provided.
- Informal dress code.