RRemoteville

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Machine Learning Engineer/Applied Scientist

Skills
Amdocs CRMArtificial IntelligenceDeep LearningExploratory Data AnalysisFeature EngineeringGenerative AIRecommender Systems
Role

What the job involves

The main requirements, responsibilities and hiring steps.

Requirements

  • Extensive industry experience as an ML engineer with expertise in one or more areas: Information Retrieval, Recommender Systems, Learning-to-Rank, Large Language Models, NLP, Deep Learning, Transfer Learning, Multi-task Learning, Graph Neural Network, Human-in-the-loop
  • Hands-on experience with traditional keyword-based search technologies and modern search paradigm using vector-based retrieval algorithms and search systems like Elasticsearch
  • Experience with deep learning frameworks such as PyTorch and TensorFlow, Large Language Models, Generative AI, Langchain, Transformer models
  • Experience with data exploration, analysis, and feature engineering
  • Excellent programming skills with python, scala, or java
  • Expertise with operationalizing, monitoring, and scaling machine learning models and pipelines in cloud ecosystems
  • Previous experience working cross-functionally with product and engineers in an agile environment
  • Experience building a variety of ML applications end to end
  • Familiar with state-of-the-art deep learning and AI research
  • Experience with distributed model training
  • Experience developing custom model architectures

Nice to have

  • State-of-the-art ML/AI research familiarity
  • Publication track record

Day to day

  • You will design and execute multi-quarter ML initiatives that deliver measurable technical, organizational, or business impacts in our Search & Recommendations domain.
  • Oversee the performance and continued optimization of our search engine and recommendation systems: build machine learning models to improve query understanding, and extract user intent and context to deliver accurate, relevant, and personalized results for users.
  • Collaborate closely with product managers, scientists, engineers, and designers to clarify requirements, provide feedback, and share data stories through presentations.