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AI Engineers

Infosys
Karnataka, IND
Full-time
Entry
Onsite
Discovered 1 weeks ago
PythonMachine learningScikit-learnTensorFlowPyTorchPandas
Free

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Key skills for this role

PythonMachine learningScikit-learn
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Primary Skills

  • The primary domain is turbomachinery, specifically steam turbines and rotors.
  • Strong proficiency in Python is required.
  • Required technologies include Scikit-learn, TensorFlow or PyTorch, Pandas, and NumPy.
  • Required knowledge includes machine learning algorithms, deep learning, statistics, probability, and NLP or computer vision techniques.

Key Responsibilities

  • Design, develop, and deploy machine learning and AI models.
  • Build and maintain data pipelines for training and inference.
  • Implement supervised and unsupervised learning algorithms.
  • Develop NLP, computer vision, or deep learning solutions.
  • Optimize models for performance, scalability, and accuracy.
  • Collaborate with data engineers, analysts, and product teams.
  • Deploy models using AWS, Azure, GCP, or other cloud platforms.
  • Monitor model performance and perform continuous improvements.
  • Conduct data preprocessing, feature engineering, and model evaluation.
  • Document models, processes, and experiments.

Required Qualifications

  • Strong proficiency in Python.
  • Experience with Scikit-learn, TensorFlow or PyTorch, Pandas, and NumPy.
  • Experience with regression, classification, and clustering algorithms.
  • Familiarity with deep learning frameworks and NLP or computer vision techniques.
  • Experience deploying models through APIs or microservices.
  • Strong understanding of statistics and probability.

Preferred Skills

  • Experience with generative AI or LLMs such as GPT, LangChain, or Hugging Face.
  • Knowledge of MLOps tools such as MLflow, Kubeflow, or Airflow.
  • Experience with Docker, Kubernetes, Spark, or PySpark.
  • Familiarity with REST APIs, microservices architecture, and cloud AI services.
  • Understanding of vector databases, embeddings, CI/CD pipelines, or DevOps practices.

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