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Infosys

AI Engineer

Career Insights for Artificial Intelligence Engineer (General)

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What they do

An Artificial Intelligence Engineer develops, tests, and deploys artificial intelligence models. May work closely with data software engineers and data professionals to train and implement AI models into existing systems or develop new applications.

$145,672 / year median in Connecticut

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Job Description

In the assigned Job Role of Data Science Consultant 1, your Area Of Responsibility will be as below:
  • Participate in data extraction, transformation, and preparation.
  • Resolve common data issues and ensure quality for model development.
  • Participate in developing models using statistical or machine learning techniques and collaborate with technology teams to operationalize them into analytics tools or scripts.
  • Participate in model testing and validation, selecting the best-performing algorithms based on statistical and business metrics.
  • Participate in the development of advanced analytics and machine learning or deep learning models including LLMs using predefined processes and tools like SAS and R/ Python.
  • Participate in defining analytics problems; execute visualization, analysis, and predictive modeling with senior support.
  • Identify data sources and extract from RDBMS and develop UI/UX for client usage.
  • Participate in model performance, while making minor adjustments, and escalate risks or compliance concerns and generate reports on deviations or schedule slippages.
  • Proactively participate in detailed documentation of model development, testing, and deployment activities for reproducibility.
  • Work closely with business and technology teams to translate requirements into actionable models, while communicating results effectively.
  • Apply predefined quality measurement frameworks, if any, to individual project tasks.
  • Participate in deploying analytics tools in test and production environment, while ensuring they meet operational requirements.
Your contribution to the team:
  • Strong analytical and problem-solving mindset with hands-on model development skills.
  • Ability to translate business needs into actionable analytics solutions.
  • Focus on data quality, validation and performance optimization.
  • Effective collaboration with business and technology stakeholders.
  • Commitment to continuous learning, knowledge sharing, fostering team development.