Machine Learning Engineer I/II - Deep Learning/LLM

Job Title : Machine Learning Engineer Gen AI, LLMs & RAG

Experience : 3 - 8 Years

Job Summary :

We are looking for a highly motivated and skilled Machine Learning Engineer with deep expertise in Machine Learning (ML), Deep Learning, Large Language Models (LLMs), Generative AI, and Retrieval-Augmented Generation (RAG). You will play a key role in designing, developing, and deploying state-of-the-art AI models and scalable systems that push the boundaries of intelligent automation and human-like interaction.

Key Responsibilities :

- Develop, train, and fine-tune advanced machine learning and deep learning models using structured and unstructured data.

- Build, evaluate, and deploy LLM-based solutions (e.g., using OpenAI, Hugging Face, LLaMA, Mistral, or similar).

- Design and implement RAG pipelines to enhance LLM capabilities with domain-specific context and external knowledge bases.

- Work with cross-functional teams (NLP researchers, data engineers, product managers) to convert business needs into ML solutions.

- Optimize inference and training performance for LLMs and Gen AI models using efficient computing and model compression techniques.

- Monitor model performance, fairness, and bias; ensure reliability in production.

- Maintain and expand reusable ML tools, frameworks, and best practices for rapid experimentation and deployment.

Mandatory Skills :

- Strong foundation in Machine Learning and Deep Learning algorithms (e.g., CNNs, RNNs, Transformers, Attention Mechanisms).

- Hands-on experience with LLMs (e.g., GPT, BERT, T5, Falcon, LLaMA, etc.).

- Proficiency in Generative AI techniques (text generation, summarization, Q&A, image-to-text, etc.).

- Experience building Retrieval-Augmented Generation (RAG) architectures using vector databases like FAISS, Pinecone, Weaviate, or Milvus.

- Deep proficiency with ML/DL frameworks : TensorFlow, PyTorch, Transformers (Hugging Face).

- Strong programming skills in Python; familiarity with libraries like LangChain, LLamaIndex, etc.

- Experience with MLOps, model versioning, and deployment in cloud environments (AWS, Azure, GCP).

- Familiarity with prompt engineering, fine-tuning LLMs, and zero-/few-shot learning methods.