Lead Ai/ml Engineer Job in Xpressbees

Lead Ai/ml Engineer

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Job Summary Duties/Responsibilities:
  • Develop scalable infrastructure, including microservices and backend, that automates training and deployment of ML models.
  • Building cloud services in Decision Support (Anomaly Detection, Time series forecasting, Fraud detection, Risk prevention, Predictive analytics), computer vision, natural language processing (NLP) and speech that work out of the box.
  • Brainstorm and Design various POCs using ML/DL/NLP solutions for new or existing enterprise problems.
  • Work with fellow data scientists/SW engineers to build out other parts of the infrastructure, effectively communicating your needs and understanding theirs and address external and internal shareholder's product challenges.
  • Build core of Artificial Intelligence and AI Services such as Decision Support, Vision, Speech, Text, NLP, NLU, and others..
  • Leverage Cloud technology AWS, GCP, Azure
  • Experiment with ML models in Python using machine learning libraries (Pytorch, Tensorflow), Big Data, Hadoop, HBase, Spark, etc
  • Work with stakeholders throughout the organization to identify opportunities for leveraging company data to drive business solutions.
  • Mine and analyze data from company databases to drive optimization and improvement of product development, marketing techniques and business strategies.
  • Assess the effectiveness and accuracy of new data sources and data gathering techniques.
  • Develop custom data models and algorithms to apply to data sets.
  • Use predictive modeling to increase and optimize customer experiences, supply chain metric and other business outcomes.
  • Develop company A/B testing framework and test model quality.
  • Coordinate with different functional teams to implement models and monitor outcomes.
  • Develop processes and tools to monitor and analyze model performance and data accuracy.
  • Develop scalable infrastructure, including microservices and backend, that automates training and deployment of ML models.
  • Brainstorm and Design various POCs using ML/DL/NLP solutions for new or existing enterprise problems.
  • Work with fellow data scientists/SW engineers to build out other parts of the infrastructure, effectively communicating your needs and understanding theirs and address external and internal shareholder's product challenges.
  • Deliver machine learning and data science projects with data science techniques and associated libraries such as AI/ ML or equivalent NLP (Natural Language Processing) packages. Such techniques include a good to phenomenal understanding of statistical models, probabilistic algorithms, classification, clustering, deep learning or related approaches as it applies to financial applications.
  • The role will encourage you to learn a wide array of capabilities, toolsets and architectural patterns for successful delivery.
Experience Required :

6 to 9 Years

Vacancy :

2 - 4 Hires

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