AI Engineer

Key Responsibilities:

• AI Solution Design and Development:
• • Designing, developing, and deploying complex AI/ML models and systems.
  • Selecting and implementing appropriate algorithms and frameworks.
  • Building scalable and efficient AI pipelines.
• Model Training and Evaluation:
• • Training and fine-tuning machine learning models using large datasets.
  • Evaluating model performance and optimizing for accuracy and efficiency.
  • Implementing model monitoring and retraining strategies.
• Software Development and Integration:
• • Integrating AI models into existing software systems.
  • Developing APIs and other interfaces for AI services.
  • Writing clean, efficient, and well-documented code.
• Research and Innovation:
• • Staying up-to-date with the latest AI research and trends.
  • Exploring and evaluating new AI technologies and techniques.
  • Contributing to research and development efforts.
• Leadership and Collaboration:
• • Leading and mentoring junior AI engineers.
  • Collaborating with cross-functional teams, including data scientists, software engineers, and product managers.
  • Communicating complex technical concepts to non-technical stakeholders.
• Solution Architecture:
• • Designing the architecture of AI powered solutions, insuring scalability, and reliability.

Requirements

Required Skills and Qualifications:

• Education:
• • A bachelor's or master's degree in computer science, artificial intelligence, machine learning, or a related field.
• Experience:
• • Significant experience in AI/ML development, typically 5+ years.
  • Proven track record of deploying AI solutions in production environments.
  • Experience with deep learning frameworks (TensorFlow, PyTorch, etc.).
  • Experience with cloud platforms (AWS, Azure, GCP).
• Technical Skills:
• • Proficiency in programming languages such as Python, Java, or C++.
  • Strong understanding of machine learning algorithms and deep learning techniques.
  • Experience with natural language processing (NLP), computer vision, or other AI subfields.
  • Knowledge of data engineering principles and practices.
  • Familiarity with containerization and orchestration tools (Docker, Kubernetes).

• Soft Skills:
• Strong problem-solving and analytical skills.
• Excellent communication and collaboration skills.
• Ability to work independently and as part of a team.
• Leadership qualities.