AI Engineer

Job Summary

You will be mainly working as an AI Engineer to support AI prototyping and deployments in the following domains: NLP/GenAI/Voice, Graph Analytics, Geospatial AI, Explainable and Responsible AI. You will work with a group of data scientists/AI engineers in a Research and Development group. The roles and task assignments in the team are dynamic, you along with other data scientists will work on strategic projects with cutting edge innovation potential to create prototypes of innovative ideas starting from ideation to development and implementation.

Essential Duties and Responsibilities

Collaborate closely with business stakeholders to understand their processes, business problems, challenges, and objectives. Identify opportunities to leverage AI and the respective domain expertise to drive efficiency, improve decision-making, and enhance customer experience. Goal alignment: Aligns stakeholder interests with the domain team's strategic objectives.

Collaborate with a cross-functional team of data scientists, engineers, and subject matter experts to create and develop innovative AI solutions.

Establishes a wide network of contacts across the organization to solicit information. Proactively reaches out and gathers key information.

Experimentation and prototyping: Drives technology development for our AI research experimentation and innovative prototype development. Brings new emerging AI tech into relevant prototypes for the company. Facilitates the data science lifecycle from data collection to model deployment in collaboration with relevant stakeholders and partners.

Can support in the design and build of MLOps infrastructure and continuous integration/continuous deployment (CI/CD) pipelines necessary to deploy, monitor and scale AI models.

Optimize the performance and scalability of AI solutions, applying knowledge of system architecture, cloud services (AWS, Azure, or GCP), and performance optimization techniques.

Integrate AI solutions with existing internal and customer-facing applications

Identify, collect, and curate relevant data from diverse sources, including open-source, government, and internal infrastructure. Ensure data quality, privacy, and security compliance.

Manage and oversee AI projects within your domain, ensuring timely delivery, adherence to quality standards, and alignment with business objectives.

Contributes to AI roadmaps of the team to provide a high-level framework for a resilient strategy

Fosters a culture of innovation and knowledge sharing. Shares insights, best practices, and technical concepts effectively to both technical and non-technical audiences related to AI infrastructure and engineering practices.

Ensures the success of the domain through management of the related projects along with appropriate stakeholder management.

Serves as a subject matter expert in AI, providing guidance, consultation, and collaboration across the bank.

Connects and collaborates with academic partners and contributes to scientific publications

Drives initiatives to promote the adoption of AI across the bank, providing guidance, training, and support to various departments.

Key Performance Indicator

Deployment of new strategic initiatives that passed the Proof of Concept phase

Prototype building, fail fast experiments, project and model deployment performance

Academic and Innovation Partnerships (resulting in e.g. awards and publications)

Improvement to team's Ways of Working and MLOps processes

Contribution to knowledge sharing through technical learning sessions conducted

Adoption of AI for enterprise use.

Educational Attainment

Preferably a Master's degree in a field related to AI and Data Science, such as Computer Science, Statistics, Data Engineering, Applied Mathematics, etc.

Tools Needed

Ability to create proof of concepts and minimum viable products or applications. Including setting up necessary infrastructures and application design.

Proficiency in Python for software development and model serving tasks. Preferably experienced in using Jupyter notebooks.

Proficiency in version control systems for collaboration like Git.

Strong ability to manage and provision cloud services on platforms like AWS, Azure, or GCP for cloud compute and MLOps infrastructure.

Familiarity with at least one data science development platform, such as Sagemaker, Databricks, Cloudera or any similar platform.

Familiarity with containerization and orchestration tools like Docker and Kubernetes.

Familiarity with machine learning frameworks like TensorFlow, PyTorch, or scikit-learn.

Basic understanding of JavaScript for debugging and interacting with web-based applications.

Proficiency in SQL for working with relational databases.

Experience with interacting with NoSQL databases.

Data visualization tools such as Matplotlib, Seaborn or Plotly

Basic familiarity with analytics tools used for dashboarding such as PowerBI or Tableau.

Soft Skills Needed

Effectively communicate and collaborate with stakeholders to understand their needs, align expectations, and drive project success. Can communicate complex AI insights to non-technical teams.

Have experience developing minimum viable product (MVP) applications to quickly iterate and gather user feedback.

Demonstrate strong technical capabilities and a willingness to learn new technologies and tools.

Possess excellent problem-solving and analytical skills to identify and address challenges in AI projects.

Ability to work independently with minimal supervision while maintaining high standards of quality and productivity.

Can easily learn and integrate new features and capabilities into existing systems.

Training and Certifications

(plus) Data Science Certifications

(plus) AWS Cloud Certifications

Experience (Indicate minimum & preferred levels of experience required to perform the job in a fully competent manner.)

Minimum 3+ years of experience developing AI projects in an industry setting