AI/ML Engineer - Data Modeling

Job Description :

We are seeking a talented and hands-on AI/ML Engineer with experience in LLM-based architectures, vector search (e. g., Pinecone), and end-to-end model deployment. You'll be working closely with our product and research teams to develop scalable NLP/NLU applications, including RAG pipelines, LLM integrations, and custom model deployments.

Responsibilities :

- Integrate with OpenAI, LLaMA, and Hugging Face models to build conversational AI solutions.

- Work with vector databases (e. g., Pinecone, Weaviate, FAISS) for embedding-based retrieval.

- Fine-tune and serve LLMs (LLaMA, GPT, etc. ) locally or via cloud deployments.

- Implement NLP/NLU tasks including summarization, classification, entity extraction, etc.

- Build and deploy ML pipelines using TensorFlow or PyTorch (preferred but not mandatory).

- Perform model evaluations, optimizations, and monitor post-deployment performance.

- Collaborate with backend and DevOps teams to deploy models using Docker, FastAPI, or other modern tools.

Requirements :

- Strong experience with LLMs (e. g., GPT-4 LLaMA, Falcon).

- Hands-on experience with RAG architectures and embedding pipelines.

- Familiarity with OpenAI APIs, LangChain, or LLM tooling frameworks.

- Working knowledge of vector stores like Pinecone, FAISS, or Weaviate.

- Proficient in Python and libraries like transformers, scikit-learn, spaCy, etc.

- Exposure to model serving & deployment - FastAPI, Flask, Docker, TorchServe, etc.

- Familiarity with NLP/ML lifecycle - from training to inference and monitoring.

- Experience with TensorFlow or PyTorch.

- Experience in deploying LLMs locally (LLaMA with llama.cpp or Ollama).

- Experience in managing Hugging Face Spaces, datasets, or model hubs.

- MLOps experience : CI/CD, model versioning, cloud deployment.