AI Engineer

AI Engineer

Krungthai Bank PCL.

We are looking for an AI Engineer to bridge the gap between data science and production systems. In this role, you will design, build, and operate scalable AI and Machine Learning solutions that power the Bank's products and services. You will work closely with Data Scientists, Data Engineers, Architects, and Application Teams to productionize ML/GenAI models (MLOps / LLMOps), ensuring they are secure, robust, observable, and compliant with banking and regulatory requirements.

AI Engineer is a role that combines skills in data science, and machine learning to design, build, test, and deploy artificial intelligence (AI) systems. AI engineers should work with the Data Scientist to understand the data and business problem and translate them into machine learning models by choosing appropriate algorithms, architectures, and frameworks. They are also responsible for preparing and processing large datasets to train the AI models - including data cleaning, transformation, and feature engineering to ensure the model learns effectively.

Responsibilities

• Directing the data gathering, data mining, and data processing processes in huge volume discover patterns, appropriate data models and Artificial Intelligence to discover the values of the data to meet the organization's unique needs.
• Leading to define requirements and scope of data analyses; presenting and reporting possible business insights to management using data visualization technologies and Artificial Intelligence (AI).
• Utilize machine learning to build predictive or prescriptive models in order to extract insights and drive business decision-making. Delivery proper design to store the data and implement the AI solution.
• Evaluating model performance by using various metrics to assess the model's accuracy, identify biases, and ensure it generalizes well to unseen data.
• Collaborate with Data Scientists to understand the data and problem domain to integrate the model into applications, and ensure the AI solution aligns with business goals.
• Stay up-to-date on the latest advancements in machine learning algorithms, deep learning techniques, and AI tools and frameworks.

1. AI System Design & End-to-End Pipelines

• Design and implement end-to-end Machine Learning and GenAI pipelines from data preparation, training, evaluation, to deployment and monitoring.
• Refactor and optimize models developed by Data Scientists (traditional ML and deep learning / LLM) for production use, focusing on efficiency, scalability, and maintainability.

2. MLOps / LLMOps & Automation

• Build and maintain CI/CD pipelines for ML models (and prompts/LLM workflows) to automate training, testing, packaging, and deployment using tools such as GitLab CI/Jenkins and cloud-native services.
• Implement model registry, versioning, and deployment strategies (blue/green, canary) to reduce risk when releasing new models.

3. API Development & Integration

• Develop, secure, and operate high-performance APIs (REST/gRPC) to expose AI services to core banking systems, digital channels, and internal applications.
• Work with solution architects and application teams to design integration patterns that meet performance, security, and reliability requirements.

4. Performance, Reliability & Observability

• Monitor production performance (latency, throughput, errors, resource utilization) and optimize serving infrastructure for high-volume banking transactions.
• Implement logging, tracing, metrics, and alerting for AI services (e.g., Prometheus/Grafana, OpenTelemetry) to enable proactive incident detection and root cause analysis.
• Support capacity planning and performance testing for AI workloads.

5. Data Engineering & Feature Pipelines

• Collaborate with Data Engineers to design and build data pipelines and feature stores for both training and real-time inference.
• Ensure data quality, lineage, and consistency across environments (development, UAT, production).

6. Governance, Security & Compliance

• Ensure AI systems comply with the Bank's security standards, PDPA, and AI Governance / Model Risk Management frameworks.
• Implement access control, encryption, and secure secrets management.
• Enable auditability and traceability of model decisions through proper logging, metadata, and documentation.

7. Emerging AI / GenAI Use Cases

• Support implementation of GenAI/NLP/OCR/Computer Vision solutions (e.g., RAG pipelines, document understanding, conversational AI).
• Evaluate and integrate cloud and on-premises AI services, model hubs, and vector databases where appropriate.

Qualifications

Education

• Bachelor's or Master's degree in Computer Science, Computer Engineering, Software Engineering, Artificial Intelligence, or a related field.

Experience

• 35 years of experience in Software Engineering, Data Engineering, or AI/ML development.
• Experience working in Financial Institutions, FinTech, or other highly regulated industries is a plus.

Technical Skills Must Have

• Strong proficiency in Python and software engineering best practices (clean code, testing, code review, documentation).
• Hands-on experience with containerization and orchestration (Docker, Kubernetes).
• Experience building and operating CI/CD pipelines for applications or ML workloads.
• Familiarity with at least one major cloud platform (AWS, GCP, or Azure) and its AI/ML-related services.
• Experience developing APIs using frameworks such as FastAPI or Flask.
• Working knowledge of relational databases (SQL) and at least one NoSQL or caching technology (e.g., Redis).

Technical Skills Nice to Have

• Experience with deep learning frameworks (TensorFlow, PyTorch) for NLP, CV, or other unstructured data.
• Knowledge of MLOps / workflow tools (e.g., MLflow, Airflow, Kubeflow, Argo).
• Experience with GenAI / LLM frameworks (e.g., LangChain, Hugging Face, vector databases) for RAG or conversational AI.
• Experience with observability tooling (e.g., Prometheus, Grafana, ELK/EFK stack).

Domain Knowledge

• Familiarity with banking or financial services use cases such as document extraction (KYC, statements), fraud detection, customer service automation, or risk analytics is preferred.

Key Competencies & Mindset

• Problem Solving & Ownership: Able to diagnose complex production issues and drive solutions end-to-end.
• Security & Reliability Mindset: Think secure by design and reliable by design in every architecture decision.
• Collaboration: Comfortable working in cross-functional, Agile teams with business, technology, risk, and operations stakeholders.
• Communication: Able to explain technical trade-offs and design choices to both technical and non-technical audiences.
• Continuous Learning: Passionate about staying up to date with MLOps/LLMOps, cloud-native, and GenAI technologies.

Contact : 062-954-1963 (K.Kanyarut)

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