We are looking for a Machine Learning Engineer with 5+ years of experience who has hands-on expertise in fine-tuning pre-trained models for production use cases. You will work on adapting large-scale models (LLMs, vision, or multimodal) to domain-specific problems, optimizing performance, and deploying them in scalable environments.

Responsibilities

• Fine-tune pre-trained machine learning models (e. g., NLP, LLMs, CV models) for domain-specific applications.
• Design and implement training pipelines for supervised fine-tuning, instruction tuning, and parameter-efficient tuning (LoRA, adapters, etc. ).
• Work with large datasets: data cleaning, preprocessing, augmentation, and labeling strategies.
• Optimize models for performance, latency, and cost (quantization, pruning, distillation).
• Evaluate model performance using appropriate metrics and benchmarking techniques.
• Deploy models into production using scalable infrastructure (APIs, batch/real-time systems).
• Collaborate with product, data, and engineering teams to translate business requirements into ML solutions.
• Monitor models in production and implement continuous improvement pipelines.
  
  

Requirements

• 5+ years of experience in Machine Learning / Deep Learning.
• Strong hands-on experience with fine-tuning pre-trained models (e. g., Transformers, CNNs, etc. ).
• Proficiency in Python and ML frameworks such as PyTorch / TensorFlow.
• Experience with libraries like Hugging Face Transformers, PEFT, or similar.
• Solid understanding of model training concepts: overfitting, regularization, and hyperparameter tuning.
• Experience with distributed training or large-scale data processing.
• Familiarity with cloud platforms (AWS / GCP / Azure).
• Strong understanding of evaluation metrics and experimentation.
  
  

Good To Have

• Experience with LLMs (instruction tuning, RLHF, prompt optimization).
• Exposure to vector databases and retrieval-augmented generation (RAG).
• Knowledge of MLOps tools (MLflow, Kubeflow, Airflow, etc. ).
• Experience optimizing models for inference (ONNX, TensorRT, quantization).
• Background in a specific domain (e. g., fintech, healthtech, e-commerce).
  
  

What We're Looking For

• Strong problem-solving mindset with a bias toward shipping solutions.
• Ability to work in ambiguous, fast-paced environments.
• Ownership of the end-to-end ML lifecycle from data to deployment.
  
  

This job was posted by Roshan Muniraj from Loop AI.