On-Device ML EngineerOpenkyber • GA, United States
On-Device ML Engineer
Openkyber • GA, United States
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job_description.job_card.job_descriptionYou can use the AI4Bharat datasets including : Samanantar BPCC Other open Indic parallel corpora Data Handling Sentence alignment (if needed) Tokenization and vocabulary preparation (SentencePiece / BPE / etc.) Model Training Training single language-pair translation models Implementing best practices for training efficiency (FP16, gradient accumulation, etc.) Hyperparameter tuning Checkpoint management and monitoring Evaluation Provide side-by-side qualitative translation samples Benchmarking against baseline models Delivery Inference scripts (Python) for quick testing Instructions for running and continuing training Documentation of preprocessing and training pipeline Optional : Dockerfile or virtual environment setup Strong experience in NLP, Transformers, and neural MT models Prior work with Indic languages (big plus) Experience with training libraries such as PyTorch, Hugging Face Transformers, Fairseq, OpenNMT, or similar Ability to handle large-scale training and dataset preprocessing Familiarity with SentencePiece, tokenization strategies, and MT evaluation metrics Ability to deliver clean, well-documented code Compute resources can be discussed (I can provide compute, or you can use yours). More language pairs may be added later as separate follow-up projects. Quality of translation is the highest priority.
Project Description :
I am looking to hire an experienced NLP / ML engineer to train high-quality machine translation models for Indic languages. The goal is to develop single language-pair models, such as :
- English Telugu
- English Hindi
(and additional language pairs, if needed)
You may choose the most suitable model architecture based on your expertise (e.g., mBART, mT5, NLLB fine-tuning, Transformer variants, etc.), as long as the final models deliver strong translation quality.
Dataset :
Scope of Work :
The freelancer will be responsible for :
Cleaning, filtering, and preprocessing datasets
Selecting an appropriate model architecture
Compute BLEU, SacreBLEU, and other relevant metrics
Final trained model weights
Requirements :
Additional Notes :
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