Intelligent Machine Learning Specialist

2 days ago


Hubli, Karnataka, India beBeeArtificial Full time ₹ 1,93,60,000 - ₹ 2,51,20,000

Immediate hiring for a leading tech company.

Role: AI/ML Engineer

Job Overview:

This position involves designing, building, and deploying machine learning models. Our focus is on practical implementation of AI solutions in the real world.

Required Skills and Qualifications:
  • Technical Competencies:
    • Proficiency in programming languages, particularly Python, with a strong understanding of its ecosystem for data science and machine learning (e.g., NumPy, Pandas, Scikit-learn, Matplotlib, Seaborn).
  • AI/ML Frameworks and Libraries:
    • Deep understanding and hands-on experience with at least one major deep learning framework (e.g., TensorFlow, PyTorch)
    • Solid understanding and practical experience with classical machine learning algorithms and Predictive Analytics.
  • Generative AI:
    • Hands-on experience with developing and deploying generative AI models using services like AWS Bedrock (e.g., for text generation, image generation, embeddings, Function Calling, AI Agents, MCP Server, LLMs like OpenAI).
    • Understanding of prompt engineering techniques for large language models.
  • Software Development Practices:
    • Proficiency in writing clean, well-documented, and testable code.
    • Experience with version control systems (e.g., Git).
  • Education and Professional Experience:
    • Bachelor's degree in a related field like Computer Science, Engineering, or Data Science.
    • Candidates should have a total of 5-7 years of experience.
    • Worked on 3 to 5 AI/ML projects and should be involved in design, development & deploying learning models and systems.
    • Designed and implemented a scalable machine learning pipeline for systems that deal with huge volumes of data.

Job Responsibilities:Model Development:

  • Design and develop machine learning systems and models.
  • Research and implement appropriate machine learning algorithms and tools.
  • Select and prepare datasets for training and testing.
  • Train and retrain models as needed.
Data Handling:
  • Analyse and preprocess data for model training.
  • Build and maintain data pipelines for efficient data flow.
  • Identify and address data quality issues.
Deployment and Monitoring:
  • Deploy models to production environments.
  • Monitor model performance and identify areas for improvement.
  • Manage and maintain deployed models.
  • Evaluate potential risks of AI/ML use cases and implement appropriate safeguards.
Collaboration and Communication:
  • Work with data scientists, software engineers, and other stakeholders.
  • Communicate technical findings and recommendations clearly.
  • Understand business needs and translate them into technical solutions.
Infrastructure and Tools:
  • Build and maintain the infrastructure required for machine learning model deployment.
  • Stay up-to-date with the latest machine learning tools and technologies.
Problem-Solving:
  • Identify and solve technical challenges related to machine learning model development and deployment.
  • Analyse model errors and design strategies to overcome them.
Ethical Considerations:
  • Ensure that AI/ML systems are developed and deployed responsibly and ethically.


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