TymeX

Tóm tắt công việc

We're seeking an AI Engineer with strong academic foundations and deep technical expertise who excels at translating research into production banking systems. This role is 80% focused on engineering excellence—deploying models, optimizing infrastructure, ensuring reliability, and solving real-world implementation challenges—and 20% on staying current with cutting-edge AI research and emerging technologies. You'll bridge the gap between state-of-the-art AI research and scalable production systems in the financial services sector.

Job Responsibilities

• AI Engineering & Deployment (80%)
  • Design, build, and deploy production-ready AI/ML systems on AWS with focus on reliability, scalability, and performance for banking applications
  • Implement and maintain MLOps pipelines using AWS services (SageMaker, Bedrock, Lambda, Step Functions) including model versioning, monitoring, and automated retraining workflows
  • Build and optimize AI solutions using AWS Bedrock, OpenAI API, and Gemini API combining with Model Context Protocol (MCP), Agent-to-Agent (A2A) protocol for various banking use cases
  • Design and implement prompt engineering frameworks and prompt management systems for LLM-based applications
  • Develop graph analysis solutions for fraud detection, customer relationship mapping, and network analysis in banking contexts
  • Debug and troubleshoot production AI systems, identifying and resolving issues in model performance, data pipelines, and AWS infrastructure
  • Build and maintain AIOps practices including automated monitoring, alerting, and incident response for AI systems on AWS Optimize model serving infrastructure for latency, throughput, and cost-efficiency using AWS services
  • Implement robust data pipelines using AWS Glue, Kinesis, and related services for training and inference Collaborate with software engineering and risk teams to integrate AI capabilities into banking products and services
  • Ensure compliance with banking regulations and security standards in all AI deployments Monitor model performance in production and implement drift detection and retraining strategies
• AI Research & Innovation (20%)
  • Stay current with latest AI research papers and breakthroughs, evaluating applicability to banking and financial services
  • Research and prototype emerging AI architectures and techniques for financial use cases
  • Evaluate new paradigms in model training, inference optimization, and architectural innovations
  • Share knowledge through technical discussions, paper reviews, and internal research presentations
  • Identify opportunities to apply cutting-edge research to improve fraud detection, customer service, risk assessment, and other banking operations

Education & Research Background

• Master's or PhD in Computer Science, AI/ML, Mathematics, Statistics, or related field with focus on machine learning, deep learning, or strong publication record or demonstrated deep understanding of AI research (thesis, projects, or contributions to the field)
• Deep theoretical knowledge of modern AI architectures and training methodologies

Technical Expertise (AI Landscape)

• Deep understanding of transformer architectures and attention mechanisms
• Strong knowledge of large language models (LLMs), multimodal models, and their architectural evolution
• Familiarity with current research trends including Agentic AI systems
• Retrieval-augmented generation (RAG) for banking context integration
• Test-time compute scaling and inference optimization
• Multimodal learning (document understanding, vision-language models)
• Long-term and short

Experience integrating and managing external AI APIs:

• OpenAI API (GPT models, embeddings, fine-tuning)
• Gemini API (Google's multimodal models)
• Expertise in prompt engineering, prompt management, and LLM-powered Agents orchestration frameworks
• Strong knowledge of graph databases and graph analysis techniques: AWS Neptune or similar graph databases Graph algorithms for fraud detection and network analysis Knowledge graph construction and reasoning

Engineering Skills

• Strong programming skills in Python and experience with ML frameworks (PyTorch, TensorFlow)
• Hands-on experience with MLOps tools (MLflow, Weights & Biases, Airflow)
• Experience with containerization and orchestration (Docker, ECS, EKS)
• Strong understanding of distributed training and GPU optimization on AWS Experience with CI/CD pipelines using AWS CodePipeline or similar
• Ability to debug complex distributed systems and data pipelines
• Strong software engineering principles and version control (Git)

Additional Experience:

• Banking and Finance: Experience in the banking or finance industry is a plus.