AI/ML Engineer

Role overview

Responsibilities

• check_circle Design, build, and deploy a range of machine learning models, including but not limited to:
• check_circle Customer churn prediction (classification), lifetime value forecasting (regression), and customer segmentation (clustering).
• check_circle Develop sophisticated models for demand forecasting, dynamic pricing, process optimization, and anomaly/fraud detection.
• check_circle Develop and fine-tune Large Language Models (LLMs) to power conversational AI for enterprise use cases, generate dynamic business reports, and create personalized marketing communications.
• check_circle Build and maintain efficient Retrieval-Augmented Generation (RAG) pipelines to provide accurate, context-aware responses from extensive enterprise knowledge bases.
• check_circle Employ web scraping techniques using Playwright or Selenium to gather essential data for competitive analysis, market trends, and model enrichment.
• check_circle Collaborate with the data engineering team to build and maintain scalable data pipelines for training and inference.
• check_circle Own the full lifecycle of machine learning models, including deployment, monitoring, and continuous improvement in a live production environment.
• check_circle Work closely with software developers to integrate AI/ML models seamlessly into client platforms and internal enterprise systems.
• check_circle Stay current with the latest advancements in AI, machine learning, and data analytics to identify new opportunities and drive innovation within the company.

Basic qualifications

• A minimum of 2 years of proven experience building and deploying machine learning models in a production setting.
• Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, or a related quantitative field.
• Strong programming skills in Python.
• Hands-on experience with core ML/DL frameworks such as Scikit-learn, TensorFlow, or PyTorch.
• Demonstrable experience with Generative AI technologies, including working with APIs from OpenAI, or similar, and HF open-source models.
• Expertise in building RAG systems and familiarity with vector databases (e.g., Pinecone, ChromaDB).
• Proficient in web scraping using Playwright and/or Selenium.
• Strong analytical and problem-solving skills with the ability to translate business problems into tangible ML-driven solutions.
• Excellent verbal and written communication skills, with the ability to explain complex technical concepts to non-technical stakeholders.