Job Type: Full-time

Experience: 0 -1 year

Timings : EST time zone

About the Role:

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.

Nice-to-Have Skills:

• Familiarity with MLOps practices (MLflow, Weights & Biases).
• Knowledge of data pipelines (Airflow, Kafka, Redis, Celery).
• Understanding of LLM safety, prompt engineering, and fine-tuning techniques.

Why Join Us?

• Work on cutting-edge NLP and AI applications with real-world impact.
• Collaborative, fast-paced learning environment with mentorship.

You can also send your resume or portfolio at [email protected]