AI Software Engineer

0 years

0 Lacs

Posted:1 day ago| Platform: Linkedin logo

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On-site

Job Type

Full Time

Job Description

AI Software Engineer


Summary

AI Software Engineer


As an AI Software Engineer, you will collaborate with engineers, data scientists, and product managers to build, optimize, and scale AI models and systems. You will work with cutting-edge tools and frameworks while applying your expertise in algorithms, data processing, and system integration to deliver high-performance AI solutions.


Key Responsibilities

  • Design, develop, test, and deploy

    AI/ML models and applications

    .
  • Build data pipelines for preprocessing, cleaning, and transforming large datasets.
  • Train, evaluate, and fine-tune machine learning models for production use.
  • Integrate AI models into applications and production systems.
  • Research and implement new AI frameworks, tools, and methodologies to enhance capabilities.
  • Debug, optimize, and scale algorithms to improve system performance.
  • Collaborate with cross-functional teams (data science, product, engineering) to deliver end-to-end AI solutions.
  • Contribute to technical documentation, code reviews, and best practices.


Qualifications

  • Bachelor’s or Master’s degree in

    Computer Science, Artificial Intelligence, Data Science, or a related field

    .
  • Strong programming skills in

    Python, Java, or C++

    .
  • Solid understanding of

    machine learning concepts

    (classification, regression, NLP, computer vision, deep learning).
  • Hands-on experience with frameworks such as

    TensorFlow, PyTorch, or scikit-learn

    .
  • Proficiency in algorithms, data structures, and object-oriented programming.
  • Experience building and deploying AI applications in real-world environments.
  • Strong analytical, problem-solving, and debugging skills.


Preferred Qualifications

  • Experience with

    large-scale data pipelines

    and distributed systems.
  • Familiarity with

    cloud platforms

    (AWS, Azure, or GCP).
  • Knowledge of APIs, microservices, and containerization (Docker, Kubernetes).
  • Exposure to

    MLOps practices

    and continuous integration/continuous deployment (CI/CD).
  • Strong interest in applied AI and innovation in emerging technologies.


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