Job Description

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• Practical experience with large language models (LLMs), prompt engineering, fine-tuning RAG-based applications, and benchmarking using frameworks like LangChain.

• Strong background in natural language processing (NLP) with experience using spaCy, word2vec, Flair, BERT.

• Formal training in machine learning, including dimensionality reduction, clustering, embeddings, and sequence classification algorithms.

• Proficiency in Python and experience working with ML frameworks like PyTorch, TensorFlow, and Hugging Face Transformers.

• Experience with cloud platforms such as AWS, GCP, or Azure.

• Understanding of data modeling principles and complex data architectures.

• Experience working with relational and NoSQL databases and vector stores (e.g., MySQL, Postgres, Solr, Elasticsearch, OpenSearch).

• Familiarity with distributed computing frameworks like Spark, Scala, or Ray (highly preferred).

• Knowledge of API development, containerization (Docker, Kubernetes), and ML deployment (highly preferred).

• Hands-on experience with ML Ops/AI Ops, including experiment tracking tools like LangFuse and DVC.

• Experience with deep learning frameworks such as PyTorch, Tensorflow and Hugging Face Transformers

Preferred Qualifications

• MS in Data Science, Computer Science, Statistics, Machine Learning, or related field

• 2+ years of relevant work experience

• Or undergraduate degree in relevant field and 4+ years of relevant work experience

Seniority level
  • Seniority level Mid-Senior level
Employment type
  • Employment type Full-time
Job function
  • Job function Information Technology
  • Industries Software Development and Data Infrastructure and Analytics

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