Job Title: AI Engineer – Data Mining Specialist

Location: [Insert Location] / Remote

Experience: 3+ years

Employment Type: Full-time

Salary: [Insert Salary Range]

Industry: Artificial Intelligence / Data Science / Analytics

Job Description:

We are seeking a talented and motivated AI Engineer with strong expertise in Data Mining to join our team. The ideal candidate will have a deep understanding of machine learning, data mining techniques, and data processing workflows. You will work closely with data scientists, product managers, and engineering teams to develop scalable AI-driven solutions that drive meaningful business outcomes.

Key Responsibilities:

• Design and develop AI models with a focus on large-scale data mining and pattern extraction.
• Apply machine learning, deep learning, and statistical modeling techniques to solve complex business problems.
• Build robust data pipelines and perform data wrangling, preprocessing, and analysis on structured and unstructured datasets.
• Work with cross-functional teams to integrate AI solutions into production systems.
• Monitor, evaluate, and continuously improve model performance and accuracy.
• Stay updated with the latest research and trends in AI, machine learning, and data mining.

Required Skills & Qualifications:

• Bachelor’s or Master’s degree in Computer Science, Data Science, AI, or a related field.
• 3+ years of experience in AI/ML, data mining, or a related domain.
• Strong programming skills in Python, with experience in libraries like Pandas, Scikit-learn, NumPy, TensorFlow, or PyTorch.
• Solid understanding of data mining techniques such as clustering, classification, association rules, and anomaly detection.
• Experience with data handling tools (SQL, Spark, Hadoop, etc.) and building data pipelines.
• Familiarity with cloud platforms (AWS, GCP, or Azure) is a plus.
• Excellent problem-solving skills and ability to work independently or in a team.

Preferred Skills:

• Knowledge of NLP, recommender systems, or graph mining techniques.
• Experience in deploying machine learning models in production.
• Familiarity with MLOps tools and practices.