About The Role Develop and deploy machine learning and AI models for real-world product use cases. Build and improve LLM-powered applications, including AI assistants and conversational systems. Develop RAG (Retrieval-Augmented Generation) pipelines using property, user, and business data. Work on semantic search, recommendation systems, and intelligent property matching. Develop lead scoring and lead–agent/property matching models. Process, clean, transform, and analyze large datasets for model development. Design and integrate embeddings, vector databases, and similarity-search systems. Build and maintain AI/ML APIs and integrate models with backend services. Evaluate model performance using appropriate metrics and continuously improve accuracy. Experiment with different ML/LLM models, prompting techniques, fine-tuning approaches, and AI frameworks. Work with engineering and product teams to convert business requirements into AI solutions. Monitor AI systems in production and troubleshoot issues related to performance, latency, and accuracy. Maintain documentation for models, datasets, experiments, and AI pipelines. Stay updated with developments in Generative AI, LLMs, NLP, and machine learning. What we're looking for Bachelor's degree in Computer Science, AI/ML, Data Science, Engineering, or a related field. 1–3 years of experience in AI/ML engineering or a closely related role. Strong programming skills in Python. Strong understanding of: Machine Learning Deep Learning NLP Model evaluation and optimization Data preprocessing and feature engineering Hands-on experience with ML libraries such as Scikit-learn, PyTorch, or TensorFlow. Practical experience working with LLMs and Generative AI. Experience with OpenAI, Gemini, Claude, Hugging Face, or similar models. Understanding of prompt engineering and LLM evaluation. Experience building RAG-based applications. Familiarity with embeddings and vector databases such as Pinecone, Qdrant, Weaviate, FAISS, or Chroma. Experience working with REST APIs and model deployment. Good understanding of SQL and databases. Familiarity with Git/GitHub and software development workflows. Strong problem-solving and analytical skills.
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