Technical Architect-ML
Macropace Technologies
Bengaluru
1d ago

Technical Architect-ML

Experience : 12 to 15yrs

Notice period : 30days max

Job Description

JOB SUMMARY Overall requirement

  • Understand the life cycle of all phases of data science starting from data collection, data preparation, data modeling, evaluation, and scaled deployment
  • A thought leader in concept formulation, suggest appropriate statistical methodology and models, evaluate multiple options, and recommend the final solution
  • Work with stakeholders, customers from requirement to solutioning to final presentation to convert sales opportunity into project
  • Provide guidance an d training to junior level ML engineers at every phase of project life cycle to deliver quality result
  • Experience of handling data at scale, defining architecture, building, and deploying models in production # Strong ability to communicate technic al concepts and implications to business stake holders
  • Define performance parameters, anticipate risks, help addressing roadblocks and guide team to navigate seamlessly
  • Overall Experience # 5 years of experience handling statistical modeling. Developing ML and Deep learning algorithms across multiple domains
  • Strong knowledge advanced analytics tools to analyze large data sets from multiple data source
  • Experience in handling unstructured data using NLP, text analytics, OCR and computer vis ion using Machine learning and Deep learning techniques
  • Strong expertise in NLP, Sentiment Analysis, Summarization, semantic search, CLV and similar models # Complete hands-on experience in Python, NumPy, Pandas, TensorFlow, Keras, Scikit, OpenCV and related technologies
  • Clear understanding of cloud concepts like Azure, AWS and Google cloud and cognitive services
  • Contribute to pre-sales activity for solution design, estimation and defending the solution architecture
  • Capable of picking up new technology areas, doing quick POC to prove the feasibility of the proposed solution
  • Must Have :

  • Python, NumPy, Pandas, TensorFlow, Keras, Scikit, OpenCV and related technologies
  • NLP, text analytics, Call summary, Sentiment analysis, CLV, Se mantic search
  • Cloud - Azure, AWS, Cognitive Services, Knowledge base
  • Enterprise integration - Integration with DB, enterprise systems
  • Design-OOPs concept, Design principles, patterns, knowledge of auto scaling.
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