Model Development and Optimization

• Design, develop, and optimize generative AI models using deep learning techniques, such as NLP, transformers, GANs, and reinforcement learning, to address specific business needs like personalized customer interactions, automated document processing, and predictive analytics.
• Continuously improve model performance by experimenting with various architectures and training methodologies.
• Design, develop, and deploy innovative AI applications using LLMs and generative AI techniques.
• Master and apply advanced prompt engineering techniques to elicit desired responses from LLMs.
• Implement and optimize RAG pipelines to ground LLM outputs in relevant and up-to-date information.
• Develop and implement Graph RAG solutions to leverage structured knowledge graphs for enhanced reasoning and context.
• Fine-tune pre-trained LLMs on specific datasets to improve performance on targeted tasks.
• Evaluate and benchmark different LLMs and generative AI models to select the best fit for specific use cases

Integration and Deployment

• Collaborate with data engineers and software developers to deploy AI models into production environments, ensuring they are scalable, efficient, and secure. 
• Work with DevOps teams to establish MLOps pipelines for continuous integration, deployment, and monitoring of AI models. 
• Ensure the seamless integration of AI solutions with existing banking platforms and systems.

Compliance and Data Governance:

• Ensure that all AI models and solutions adhere to the bank's data privacy, security, and compliance standards, such as GDPR, AML, and KYC. 
• Develop transparent, explainable AI (XAI) solutions that meet regulatory requirements and are aligned with ethical AI guidelines. 
• Participate in model risk management processes, including validation and documentation.

NA

• 8+ years of overall ML experience with min 3+ experience in AI/ML engineering, with a focus on generative models and advanced machine learning techniques.
• Proven experience developing and deploying AI models in a production environment, preferably within the banking or financial services sector.
• Experience working with large datasets, data pipelines, and cloud-based AI platforms (e.g., AWS Sagemaker, Azure ML, Google AI Platform).

Technical/Functional Skills:

• Proficiency in programming languages such as Python, R, or Java, and experience with deep learning frameworks (e.g., TensorFlow, PyTorch).
• Strong knowledge of NLP, computer vision, and other generative AI techniques (e.g., GPT, BERT, GANs).
• Proven expertise in prompt engineering, RAG, Graph RAG, and fine-tuning techniques.
• Strong understanding of LLM architectures and their underlying principles.
• Experience with various LLMs (e.g., Gemini, GPT, Llama, etc.) and related tools and frameworks (e.g., LangChain, Haystack).
• Proficiency in Python and experience with relevant libraries (e.g., TensorFlow, PyTorch, Transformers).
• Experience with vector databases and knowledge graph technologies.
• Experience with MLOps tools and practices, including version control, CI/CD, containerization (e.g., Docker), and orchestration (e.g., Kubernetes).
• Strong understanding of data and AI technologies, trends, and best practices. Experience in working with data-driven projects and initiatives.
• Computer Literacy – knowledge in Microsoft Office Excel, Words, & Powerpoint

Personal skills (Soft Competencies [Core/Leadership]):

• Ability to manage AI/ML projects, including model development, testing, deployment, and monitoring.
• Strong understanding of agile methodologies and experience working in cross-functional teams.
• Excellent analytical, problem-solving, and communication skills.
• Ability to work collaboratively with technical and non-technical stakeholders.
• Proactive mindset with a strong drive for innovation and continuous learning.