About the Role:

We are hiring AI/ML Engineers to join a growing remote-first product and research team working on various AI/ML-driven applications across domains like:

• Real Estate Intelligence

• Fintech

• Predictive Analytics

• Generative AI

• AI Assistants & Agents

• Data Automation Tools

This is a hands-on engineering role best suited for freshers or early-stage professionals looking to gain deep experience with practical ML model development, LLM integrations, and production deployment.

⸻

🛠 Responsibilities:

• Assist in training and tuning ML models using scikit-learn, TensorFlow, or PyTorch

• Work with structured and unstructured datasets using Pandas, NumPy, SQL, and APIs

• Build and test AI pipelines: preprocessing → modeling → evaluation → deployment

• Integrate AI models into microservices (FastAPI/Flask)

• Use LLM APIs (OpenAI, Anthropic, Gemini, Mistral) for building AI assistants and tools

• Implement vector search and semantic search using Pinecone or ChromaDB

• Write clean, reusable, and well-documented code in Python

⸻

✅ Requirements:

• Degree in Computer Science, Data Science, Engineering, or equivalent practical skills

• Solid understanding of ML concepts: regression, classification, clustering, etc.

• Experience with:

• Python and Jupyter notebooks

• Pandas, Numpy, Matplotlib

• At least one ML library: scikit-learn, TensorFlow, or PyTorch

• Good grasp of APIs and working with JSON/REST endpoints

• Strong problem-solving ability and attention to detail

• Basic version control with Git

⸻

🌟 Nice-to-Haves:

• Exposure to OpenAI, LangChain, Hugging Face, or LlamaIndex

• Familiarity with Pinecone, ChromaDB, or vector databases

• Understanding of cloud deployment (Google Cloud, AWS, or Firebase)

• Participation in hackathons, Kaggle competitions, or personal ML projects

• Interest in domain-specific AI (real estate, finance, e-commerce, etc.)

⸻

🧠 You’ll Learn & Work With:

• AI prompt engineering & chaining logic

• LLM-driven workflows using LangChain / RAG pipelines

• End-to-end ML lifecycle (train → deploy → monitor)

• Generative AI and AI copilots

• FastAPI for microservice integration