Key Responsibilities:
Leadership and Strategy:
- Define and drive the AI/ML and GenAI strategy in alignment with the companys overall vision and business goals.
- Lead and mentor a team of AI/ML engineers and data scientists, fostering a culture of innovation and excellence.
- Collaborate with cross-functional teams to integrate AI/ML and GenAI capabilities into our LCNC and Pro Code platform.
Technical Expertise:
- Oversee the design, development, and deployment of AI/ML models and GenAI technologies to solve complex business problems.
- Ensure the scalability, reliability, and performance of AI/ML solutions.
- Stay abreast of the latest advancements in AI/ML and GenAI technologies and evaluate their potential impact on our platform.
Project Management:
- Manage multiple AI/ML projects simultaneously, ensuring they are delivered on time, within scope, and within budget.
- Develop and maintain project plans, track progress, and report on project status to senior management.
Stakeholder Engagement:
- Work closely with internal stakeholders, including product management, engineering, and business development teams, to understand their needs and provide AI/ML solutions.
- Engage with external stakeholders, such as clients and partners, to demonstrate the value of our AI/ML capabilities.
Compliance and Risk Management:
- Ensure that all AI/ML initiatives comply with relevant regulations and industry standards.
- Identify and mitigate risks associated with AI/ML implementations.
Qualification Experience:
Bachelors or Masters degree in Computer Science, Data Science, Engineering, or a related field. Ph.D. is a plus.
10 to 15 years of experience in AI/ML technologies, with a significant portion spent in the banking or financial services industry.
Required Skills:
- Proven leadership experience, including leading technical teams and managing complex projects.
- Strong expertise in machine learning algorithms, data analysis, natural language processing, and generative AI technologies.
- Hands-on experience with AI/ML frameworks and tools such as TensorFlow, PyTorch, scikit-learn, Keras, and others.
- Proficient in programming languages such as Python, R, and SQL.
- Experience with data preprocessing, feature engineering, and model evaluation techniques.
- Familiarity with MLOps practices and tools for model deployment, monitoring, and maintenance.
- Knowledge of cloud computing platforms and services (e.g., AWS, Azure, Google Cloud) for AI/ML workloads.
- Understanding of ethical AI principles and data privacy regulations.
- Excellent problem-solving skills and the ability to think strategically and analytically.
- Strong communication and interpersonal skills, with the ability to effectively convey technical concepts to non-technical stakeholders.
Preferred Skills:
- Experience with Low-Code/No-Code platforms.
- Knowledge of banking regulations and compliance requirements.
- Experience with big data technologies (e.g., Hadoop, Spark) and data visualization tools (e.g., Tableau, Power BI).
