About the job
About the Role
We are on the lookout for a talented Senior AI Engineer who possesses a robust academic background and extensive technical knowledge in AI. This role is primarily focused on transforming cutting-edge research into efficient production banking systems. With 80% of your time dedicated to engineering excellence, deploying models, optimizing infrastructure, ensuring system reliability, and tackling real-world implementation challenges, and 20% on keeping abreast of the latest AI advancements and technologies, you'll be instrumental in connecting top-tier AI research with scalable solutions in the financial services domain.
Key Responsibilities
- AI Engineering & Deployment (80%):
- Design, develop, and implement production-ready AI/ML systems on AWS, with an emphasis on reliability, scalability, and performance tailored for banking applications.
- Establish and maintain MLOps pipelines utilizing AWS services (SageMaker, Bedrock, Lambda, Step Functions), focusing on model versioning, monitoring, and automated retraining workflows.
- Construct and fine-tune AI solutions leveraging AWS Bedrock, OpenAI API, and Gemini API, integrating Model Context Protocol (MCP) and Agent-to-Agent (A2A) protocols for diverse banking applications.
- Create and implement prompt engineering frameworks and management systems for LLM-driven applications.
- Develop graph analysis solutions for fraud detection, customer relationship mapping, and network analysis within banking contexts.
- Diagnose and troubleshoot production AI systems, addressing issues related to model performance, data pipelines, and AWS infrastructure.
- Establish and uphold AIOps practices, including automated monitoring, alerting, and incident response for AI systems on AWS.
- Optimize model serving infrastructure for latency, throughput, and cost-effectiveness using AWS services.
- Implement robust data pipelines using AWS Glue, Kinesis, and similar services for training and inference.
- Collaborate with software engineering and risk teams to fuse AI capabilities into banking products and services.
- Ensure adherence to banking regulations and security standards across all AI deployments.
- Monitor model performance in production, executing drift detection and retraining strategies.
- AI Research & Innovation (20%):
- Stay updated with the latest AI research papers and breakthroughs, assessing their relevance to banking and financial services.
- Explore and prototype emerging AI architectures and techniques for financial applications.
- Evaluate novel paradigms in model training, inference optimization, and architectural innovations.
- Share insights through technical discussions, paper reviews, and internal research presentations.
- Identify opportunities to integrate cutting-edge research to enhance fraud detection, customer service, risk assessment, and other banking operations.
