AI and machine learning

Overview:

The AI and Machine Learning professional is responsible for designing, developing, and deploying intelligent systems that leverage data-driven algorithms to automate tasks, generate insights, and enhance business decision-making. The role involves working with large datasets, building predictive models, and collaborating with cross-functional teams to integrate AI solutions into products and services.

Key Responsibilities:

Model Development & Implementation

• Design, develop, and train machine learning (ML) and deep learning (DL) models for various applications such as prediction, classification, recommendation, or NLP.
• Implement scalable AI pipelines for data processing, model training, and deployment.
• Perform feature engineering, model optimization, and performance tuning.
• Utilize frameworks such as TensorFlow, PyTorch, scikit-learn, or Keras for model development.

Data Management & Analysis

• Collect, clean, and preprocess structured and unstructured data from multiple sources.
• Perform exploratory data analysis (EDA) to uncover patterns and insights.
• Work with large datasets using SQL, Python (Pandas, NumPy), or big data tools like Spark.
• Ensure data integrity, quality, and consistency throughout the model lifecycle.

Research & Innovation

• Stay updated on the latest trends and advancements in AI, ML, and data science.
• Experiment with new algorithms, architectures, and techniques to improve model performance.
• Contribute to building innovative prototypes and proof-of-concept (POC) solutions.

Collaboration & Deployment

• Collaborate with data engineers, software developers, and product teams to integrate ML models into production environments.
• Deploy and monitor models using MLOps tools and practices (e.g., MLflow, Docker, Kubernetes, AWS SageMaker).
• Communicate results and insights clearly to both technical and non-technical stakeholders.

Evaluation & Maintenance

• Evaluate model performance using appropriate metrics (accuracy, precision, recall, F1, AUC, etc.).
• Continuously retrain and update models based on new data and feedback.
• Troubleshoot performance issues and ensure model stability in production.

Skills & Qualifications:

• Bachelor’s or Master’s degree in Computer Science, Data Science, Artificial Intelligence, or a related field.
• Strong programming skills in Python, R, or Java.
• Hands-on experience with ML/DL frameworks (TensorFlow, PyTorch, scikit-learn).
• Solid understanding of data structures, algorithms, and statistical modeling.
• Familiarity with cloud platforms (AWS, Azure, GCP) for deploying AI models.
• Strong analytical and problem-solving skills.
• Excellent communication and teamwork abilities.