Machine Learning Engineer

About the job

About UsWe are building advanced transformer models focused on multilingual translation and custom transformer architectures. Our team works on large-scale NLP and transformer-based models with a strong focus on research, experimentation, model optimization, and production-grade AI systems. We are looking for a highly skilled Machine Learning Engineer who is passionate about designing, developing, and optimizing AI/ML models, working with transformer architectures, and contributing to cutting-edge NLP research and development.This is a core ML engineering role not a prompt engineering or API integration role. Role OverviewAs a Machine Learning Engineer, you will be responsible for developing, training, fine-tuning, evaluating, and deploying transformer-based NLP models and Large Language Models (LLMs). You will work closely with AI researchers, data scientists, and software engineering teams to build scalable multilingual AI solutions. The ideal candidate should have strong hands-on experience with ML model development pipelines, data preprocessing, tokenization techniques, transformer architectures, GPU-based training environments, and model optimization techniques. A strong interest in experimentation, improving model performance, and deploying AI systems at scale is essential.

Key Responsibilities

• Design, develop, and optimize transformer-based AI/ML models for NLP and multilingual applications.
• Build and maintain end-to-end machine learning pipelines for data processing, model training, evaluation, and deployment.
• Train and fine-tune transformer models on large-scale custom datasets.
• Work with Seq2Seq, encoder-decoder architectures, attention mechanisms, and modern LLM architectures.
• Optimize models using techniques such as LoRA, QLoRA, PEFT, quantization, and distributed training approaches.
• Develop tokenization pipelines using BPE, SentencePiece, and other subword tokenization methods.
• Evaluate model performance using BLEU, perplexity, accuracy, and custom evaluation benchmarks.
• Collaborate with infrastructure teams to manage GPU environments, distributed training, and scalable AI deployments.
• Implement efficient model serving, inference optimization, and production-ready ML solutions.
• Work closely with researchers and engineers to experiment with new architectures, improve existing models, and enhance system performance.

Required Qualifications

• 2+ years of experience in Machine Learning, Deep Learning, NLP, or AI model development.