Machine Learning Engineer

2 years

0 Lacs

Posted:15 hours ago| Platform: Linkedin logo

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Work Mode

On-site

Job Type

Full Time

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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