We are seeking a highly skilled AI/ML Developer with expertise in Python and a strong background in developing machine learning and LLM-based AI applications. You will work on cutting-edge technologies including Retrieval Augmented Generation (RAG), Lang Chain, and Vector Databases, helping to build scalable AI systems for production use.

Key Responsibilities :

- Design and implement ML models for various tasks, including classification, regression, clustering, NLP, and deep learning.

- Develop and deploy AI applications with LangChain, RAG architectures, and LLMs (such as OpenAI and Huggingface).

- Implement vector database solutions (e.g., FAISS, Pinecone, Milvus) for efficient semantic search and data retrieval.

- Work on prompt engineering, retrieval chains, and enhance AI-driven automation for real-world applications.

- Write clean, modular, production-ready Python code with high scalability and performance.

- Collaborate closely with cross-functional teams, including data scientists, software engineers, and product managers.

- Optimize AI models for inference and deployment in - cloud environments (AWS, Azure, GCP).

- If interested, contribute to - React.js- front-end development for AI-powered applications.

Required Skills & Qualifications :

- 7+ years of experience in AI/ML development with Python

- Strong expertise in Machine Learning algorithms including XGBoost, SVM, Random Forests, and Logistic Regression.

- Deep learning proficiency with Ns, RNNs, Transformers (BERT, GPT).

- Unsupervised learning techniques (e.g., K-Means, PCA).

Hands-on experience with :

- RAG architectures, LangChain, and prompt engineering

- Vector search & semantic retrieval (e.g., BM25, DPR)

- Python ML libraries : scikit-learn, TensorFlow/PyTorch, Huggingface Transformers

- Familiarity with Cloud-based AI development (AWS, Azure, or GCP).

Nice to Have :

- Experience with React.js for integrating AI models into web applications.

- Exposure to MLOps tools (MLflow, Kubeflow, Airflow).

- Knowledge of contrastive/self-supervised learning (SimCLR, CLIP).