Design and develop complex AI models and systems while leading junior engineers and collaborating with cross-functional teams to deploy innovative solutions.
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.
