Key Responsibilities: 

•AI/ML Solution Development: Design, develop, and deploy AI and machine learning models and algorithms to solve complex business problems. 

•Technical Leadership: Provide technical leadership and mentorship to junior engineers and data scientists, guiding them in best practices and advanced techniques. 

•Research & Innovation: Stay up to date with the latest advancements in AI and machine learning technologies and apply them to improve existing systems or develop new solutions. 

•Collaboration: Work closely with product managers, software engineers, and other stakeholders to define project requirements, create technical specifications, and ensure successful implementation of AI solutions. 

•Data Analysis & Preprocessing: Perform data analysis, data preprocessing, and feature engineering to prepare datasets for machine learning models. 

•Model Training & Evaluation: Train, validate, and fine-tune machine learning models, ensuring they meet performance and accuracy requirements. 

•Deployment & Monitoring: Deploy AI models into production environments, and monitor their performance, making adjustments as necessary to maintain optimal operation. 

•Documentation: Document AI models, algorithms, and methodologies to ensure reproducibility and knowledge sharing within the team. 

•Compliance & Ethics: Ensure AI solutions adhere to ethical guidelines, data privacy regulations, and industry standards. 

Qualifications: 

•Education: Bachelor’s or Master’s degree in Computer Science, Data Science, Electrical Engineering, or a related field. PhD is a plus. 

•Experience: Minimum of 8 years of experience in AI and machine learning, with a proven track record of deploying AI solutions in a production environment.  Experience in designing and managing scalable platforms for AI/ML solutions.

Technical Skills: 

•Strong proficiency in programming languages such as Python, or C++. 

•Extensive experience with machine learning frameworks and libraries (e.g., TensorFlow, PyTorch, scikit-learn). 

•Strong understanding of statistical analysis, data mining, and data visualization techniques. 

•Knowledge of cloud platforms (e.g., AWS, GCP, Azure) and containerization (e.g., Docker, Kubernetes). 

•Familiarity with version control systems (e.g., Git) and software development methodologies (e.g., Agile, Scrum).