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Senior Machine Learning Engineer (NLP) - Python, AWS - Artificial Intelligence / Machine Learning market

5+ years
Full-time (40h)
Artificial Intelligence / Machine Learning
Full Remote
Python
AWS
Azure
GCP
Tensorflow
OpenAI
NLP

Requirements

Must-haves

  • 5+ years of experience in AI/ML
  • Proficiency in Python
  • Proficiency in AWS, GCP, or Azure for model deployment, scaling, and storage solutions
  • Experience with NLP, data extraction, and information retrieval
  • Experience developing, deploying, and optimizing AI models in production environments
  • Experience in AI/ML frameworks (e.g. PyTorch, TensorFlow, Hugging Face Transformers, etc.)
  • Hands-on experience with text classification, named entity recognition (NER), sentiment analysis, and language model fine-tuning
  • Hands-on experience with search indexing techniques and vector databases (e.g. Pinecone, Weaviate)
  • Deep understanding of NLP models, transformer architectures (e.g. BERT, GPT), embeddings (image, word, sentence), and similarity metrics
  • Familiarity with XAI concepts and implementing transparency and accountability in AI models
  • Excellent analytical and problem-solving skills
  • Ability to convey complex AI concepts to non-technical stakeholders
  • Strong communication skills in both spoken and written English
  • Bachelor's or Master's Degree in Computer Science, Data Science, Artificial Intelligence, or equivalent

Nice-to-haves

  • Startup experience
  • Experience with OCR tools like Tesseract and Amazon Textract
  • Experience in processing audio and video data
  • Experience building modular AI systems and commercializing AI models
  • Knowledge of information retrieval systems and search engine optimization
  • Ph.D. in Computer Science, Data Science, Artificial Intelligence, or equivalent

What you will work on

  • Design, develop, and optimize AI and machine learning models for applications (e.g. NLP, multi-modal data processing, semantic search, etc.)
  • Build and maintain robust data extraction, transformation, and enrichment pipelines
  • Implement and enhance systems for information retrieval, embedding management, and semantic search
  • Develop, fine-tune, and deploy NLP models for tasks including text classification, named entity recognition (NER), and contextual understanding
  • Incorporate explainability into AI models to ensure transparency and accountability in AI-driven insights
  • Develop scalable AI solutions leveraging cloud infrastructure, optimizing models for large-scale and high-performance applications
  • Work with product managers, data scientists, and engineers to align AI initiatives with product goals while mentoring junior team members
  • Stay updated with the latest advancements in AI, NLP, XAI, and data processing
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