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 and implement AI applications using large language models (LLMs) and other generative AI technologies
•  Develop and optimize prompts for various use cases to enhance model performance
•  Build and maintain Retrieval Augmented Generation (RAG) systems to improve AI responses with external knowledge
•  Implement Graph RAG solutions for complex knowledge structures
•  Assist in model fine-tuning projects to adapt pre-trained models for specific use cases

•  Collaborate with cross-functional teams to gather requirements and implement AI solutions

   

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.

 This section determines the significant quantities on which the job has some direct and indirect impact. Thus, please provide numerical data which gives a feeling for the scope and the scale of the job. Eg, 

Financial: Annual budgets, cost, annual turnover, AUM, sales turnover etc 

Non-financial:significant volume associated with the job

Qualifications 

(Basic Degree/Diploma etc)

NA

• 5+ years of overall ML experience with min 1-2+ 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).

Competencies/Skills

(Essential to succeed in this job)

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).

Practical experience with:

   - Python programming

   - LLM frameworks (e.g., LangChain, LlamaIndex)

   - Vector databases (e.g., Pinecone, Weaviate)

   - Prompt engineering techniques

• 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.