Responsibilities:

    Design and implement ML models: Analyze business needs, select appropriate algorithms and data sets, train and evaluate models for accuracy and efficiency.

    Develop and integrate ML applications: Write high-quality code to integrate models into production systems, ensuring scalability and performance.
    Develop and manage data pipelines: Automate data ingestion, preprocessing, and feature engineering for model training and evaluation.
    Monitor and optimize ML models: Track performance metrics, identify and address issues, and fine-tune models for improved accuracy and efficiency.
    Stay up-to-date with the latest AI/ML trends: Participate in research, attend conferences, and actively learn about emerging technologies and best practices.
    Collaborate effectively: Work closely with data scientists, engineers, and other team members to design, develop, and deploy AI solutions.
    Communicate effectively: Document your work clearly and concisely, present findings to stakeholders, and explain complex technical concepts in layman’s terms.

Required Skills:

    Strong proficiency in Python (including libraries like NumPy, pandas, Scikit-learn)
    Deep understanding of machine learning algorithms, deep learning architectures and concepts
    Experience with popular ML frameworks (e.g., TensorFlow, PyTorch, Keras)
    Familiarity with statistical analysis and data manipulation techniques
    Solid software development skills (e.g., object-oriented programming, version control)
    Excellent problem-solving and analytical skills
    Strong communication and collaboration skills
    Ability to work independently and manage multiple tasks simultaneously
    Experience with natural language processing (NLP) or computer vision (CV)

Technology Stack:

    Primary Programming Language: Python
    Machine Learning Frameworks: TensorFlow, PyTorch, Keras
    Data Manipulation Libraries: Pandas, NumPy, Scikit-learn
    Additional Tools: (Cloud platform, Version control system, Database, etc.)