AI overview

Design and implement complex AI pipelines while leading a team of engineers and collaborating with cross-functional professionals to deliver innovative AI 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.