Description:
We are looking for a Junior Machine Learning Engineer with expertise in Natural Language Processing (NLP), Large Language Models (LLMs), and Retrieval-Augmented Generation (RAG). The ideal candidate will work on developing, deploying, and optimizing ML applications while integrating APIs and ensuring scalable deployments in cloud environments.
Key Responsibilities:
- Develop and fine-tune NLP models using transformers, embeddings, and deep learning techniques.
- Implement and optimize LLMs (e.g., GPT, LLaMA, Falcon, Mistral) for various applications.
- Design and build RAG pipelines with vector databases (Milvus, Pinecone, Qdrant, ChromaDB).
- Develop and deploy APIs using FastAPI and Django for ML model serving.
- Optimize model inference and latency for real-time applications.
- Utilize Docker, and CI/CD pipelines for scalable ML deployments.
- Collaborate with cross-functional teams for end-to-end ML system integration.
- Monitor and enhance model performance, scalability, and efficiency.
Required Skills:
- Programming: Python (TensorFlow, PyTorch, Hugging Face, LangChain).
- Experience with LangChain, LlamaIndex, OpenAI API.
- NLP & LLMs: Experience with transformer models, fine-tuning, and embeddings.
- APIs & Web Frameworks: Proficiency in FastAPI, Django, Flask.
- Deployment & Cloud: AWS services (Lambda, EC2, S3, IAM), Docker
- Databases & Vector Search: PostgreSQL, MongoDB, FAISS, Pinecone, ChromaDB.
- Version Control & CI/CD: Git, GitHub Actions, Docker, Jenkins.