AI/ML Engineer

• AI/ML Solution Development:

  Design, build, and optimize machine learning models and algorithms for various applications, including natural language processing, computer vision, recommendation systems, and predictive analytics.

• Data Collection and Preprocessing:

  Collaborate with data engineers to identify, acquire, and preprocess datasets, ensuring high quality and suitability for model training.

• Model Training and Evaluation:

  Implement state-of-the-art AI/ML algorithms using frameworks like TensorFlow, PyTorch, or scikit-learn, and conduct rigorous testing and evaluation to measure performance and accuracy.

• Deployment and Integration:

  Deploy AI models into production environments and integrate them into existing systems for efficient and reliable execution.

• Algorithm Optimization:

  Continuously optimize and improve existing AI/ML models to enhance performance and scalability.

• Research and Innovation:

  Stay current with the latest advancements in AI/ML research and technology, applying innovative approaches to address business challenges.

• Collaborative Teamwork:

  Work closely with cross-functional teams to understand business requirements, develop technical solutions, and communicate project progress effectively.

• Documentation:

  Maintain clear and concise documentation of AI/ML models, algorithms, and codebases to facilitate understanding and future enhancements.

• Data Privacy and Security:

  Ensure compliance with data privacy and security protocols, protecting sensitive information.

Requirements:

• Education:

  Bachelors or Masters degree in Computer Science, Data Science, Machine Learning, or a related field. A Ph.D. in a relevant domain is a plus.

• Technical Skills:

  Proficiency in programming languages such as Python, R, or Java, with experience in popular AI/ML libraries and frameworks (e.g., TensorFlow, PyTorch, Keras, scikit-learn).

• Machine Learning Expertise:

  Strong understanding of various machine learning algorithms, deep learning architectures, and statistical methods. Experience with supervised and unsupervised learning techniques.

• Data Handling:

  Knowledge of data preprocessing, feature engineering, and data visualization techniques.

• Software Development:

  Familiarity with software development principles, version control, and agile methodologies.

• Problem-Solving Skills:

  Ability to analyze complex problems, develop innovative solutions, and troubleshoot issues effectively.

• Communication:

  Excellent verbal and written communication skills, capable of conveying technical concepts to both technical and non-technical stakeholders.

• Team Player:

  Collaborative mindset with the ability to work well within a team environment.

Job Type:

• Full-time

Schedule:

• Day shift