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Machine Learning Engineer

Machine Learning Engineer

RecroBengaluru, India
30+ days ago
Job description

Who You Are

You're an ML Research Engineer with 2+ years of experience who bridges the gap between

cutting-edge research and production systems. You're passionate about training models that

perform exceptionally well not just on benchmarks but in real-world applications. You enjoy

diving deep into model architectures, experimenting with training techniques, and building

robust evaluation frameworks that ensure model reliability in critical applications.

Responsibilities

  • Train and fine-tune models for speech recognition (ASR) or NLP tasks including intent

classification, Named Entity Recognition (NER), and entity linking to knowledge bases in

multilingual healthcare contexts

  • Build data pipelines for dataset collection, annotation, augmentation, and synthetic data
  • generation to address multilingual and low-resource challenges

  • Design and implement comprehensive evaluation frameworks to measure model
  • performance across precision, recall, F1, and domain-specific benchmarks

  • Research and implement state-of-the-art techniques from academic papers to improve
  • model performance on ASR, NER, intent classification, or entity linking tasks

  • Optimize models through fine-tuning techniques (LoRA, QLoRA, full fine-tuning) and
  • architecture experiments for production deployment

  • Collaborate with AI engineers to deploy optimized models into production systems and
  • ensure reliability in critical healthcare applications

    Qualifications

    Required

  • 2+ years of experience in ML / DL with focus on training and fine-tuning production
  • models

  • Deep expertise in speech recognition systems (ASR) or natural language processing
  • (NLP), including transformer architectures

  • Strong understanding of NER, intent classification, or entity linking systems with
  • hands-on experience building these components

  • Proven experience with model training frameworks (PyTorch, TensorFlow) and
  • distributed training

  • Strong understanding of evaluation metrics and ability to design domain-specific
  • benchmarks

  • Experience with modern speech models (Whisper, Wav2Vec2, Conformer) or NLP
  • models for NER / intent classification (BERT, RoBERTa, BiLSTM-CRF)

  • Experience with LLM fine-tuning techniques (LoRA, QLoRA, full fine-tuning) or
  • knowledge base integration methods

  • Proficiency in handling multilingual datasets and cross-lingual transfer learning
  • Track record of improving model performance through data engineering and
  • augmentation strategies

    Nice to Have

  • Published research or significant contributions to open-source ML projects
  • Experience with entity linking to knowledge bases (Wikipedia, DBpedia, domain-specific
  • ontologies)

  • Experience with model optimization techniques (quantization, distillation, pruning)
  • Background in low-resource language modeling or few-shot learning approaches
  • Experience building evaluation frameworks for production ML systems
  • Understanding of information extraction pipelines and knowledge graph construction
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    Machine Learning Engineer • Bengaluru, India

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