Solution Architect (Machine Learning and AI stream)

Mid / Senior


In Office

Meytier Premier Employer

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About This Workplace

Meytier Partner

Key Responsibilities:
  • 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 and 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 technical concepts and implications to business stake holders
  • Define performance parameters, anticipate risks, help addressing road blocks and guide team to navigate seamlessly
Desired Profile:
  • 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 vision using Machine learning and Deep learning techniques.
  • 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:
    1. Python, NumPy, Pandas, TensorFlow, Keras, Scikit, OpenCV and related technologies
    2. NLP, text analytics, OCR and computer vision
    3. Cloud – Azure, AWS, Cognitive Services, Knowledge base
    4. Data Base - SQL, NoSQL Knowledge
    5. Design – OOPs concept, Design principles, patterns, knowledge of auto scaling
    6. Experience in pre-sales activity

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