ML Engineer – LoRA Fine-Tuning | Python, PyTorch & Hugging Face

ML Engineer – LoRA Fine\-Tuning \| Python, PyTorch \& Hugging Face

• *Experience:** 6\+ Years
• *Duration:** 6 Months (Extendable)
• *Work Timing:** 11:00 AM – 8:00 PM IST
• *Monthly Budget:** ₹1,00,000 – ₹1,11,000
• *Work Model:** Offshore Partner Engagement

Job Summary

We are looking for an experienced **Machine Learning Engineer** with strong expertise in LLM fine\-tuning and Parameter\-Efficient Fine\-Tuning (PEFT), particularly LoRA (Low\-Rank Adaptation). The candidate will be responsible for building and operationalizing a LoRA fine\-tuning pipeline to adapt a frozen foundation model for clinical\-domain applications.

The role involves data preprocessing, tokenization, training pipeline implementation, experiment tracking, checkpoint management, adapter packaging, and integration with inference and serving workflows.

Key Responsibilities

• Implement and maintain LoRA fine\-tuning pipelines against frozen foundation models.
• Configure LoRA adapters, training loops, and hyperparameters using PyTorch and Hugging Face PEFT.
• Develop data preprocessing, tokenization, and training/validation splits for clinical datasets.
• Execute, monitor, and optimize training runs, including checkpointing and experiment tracking.
• Maintain model checkpoints, experiment records, and adapter versioning.
• Package trained LoRA adapters as portable artifacts for repeatable deployment.
• Integrate LoRA adapters into model inference and serving pipelines.
• Collaborate with AWS Data Scientists on evaluation frameworks and model\-quality metrics.
• Follow best practices for reproducibility, training efficiency, and model evaluation.

Required Skills

• Strong proficiency in Python and PyTorch.
• Hands\-on experience with Hugging Face Transformers and PEFT.
• Practical experience implementing LoRA and parameter\-efficient fine\-tuning techniques.
• Experience with LLM training, fine\-tuning, and evaluation.
• Knowledge of data preprocessing, tokenization, and dataset preparation.
• Experience with training checkpoints, model versioning, and adapter deployment.
• Familiarity with experiment tracking tools such as MLflow or Weights \& Biases.
• Understanding of model inference and serving workflows.

Preferred Qualifications

• Experience working with clinical or healthcare\-related datasets.
• Familiarity with AWS\-based machine learning workflows.
• Experience optimizing LLM training pipelines and evaluating model performance.

Language Requirement

Candidates must meet the project's mandatory language requirement. Language proficiency will be confirmed during screening.

Engagement Details

• 6\-month contract with the possibility of extension.
• Working hours: 11:00 AM – 8:00 PM IST.
• Offshore partner engagement.
• Monthly rate: ₹1,00,000–₹1,12,000, depending on experience and suitability.

Pay: ₹90,000\.00 \- ₹100,000\.00 per month

Work Location: Remote