Senior Machine Learning Engineer I (Finance)
At a glance
Senior Machine Learning Engineering role at Makro PRO. Bangkok · full-time.
Pay not stated Machine Learning Engineer salary
aimlroles summary, based on the employer's posting.
What you'll do
- Assess AI tools for finance tasks, testing accuracy, reliability, safety, response time and business fit
- Create backend services and APIs that link AI workflows to finance platforms, including Oracle Fusion
- Build finance copilots, retrieval features and document tools for invoices or contracts
- Track production behavior, troubleshoot delays and failures, and keep evaluation and experiment records
- Shape Finance and product requests into dependable AI solutions, then document their design and operation
What you bring
- Five to eight years working in software engineering, ML engineering or AI engineering
- Strong Python capability and experience designing backend services for production
- A record of launching AI or LLM applications into production environments
- Practical experience with vector databases, embeddings or retrieval-based systems
Who this fits
This senior engineering role suits someone focused on production AI applications and backend systems rather than creating custom machine-learning models. You'll work with Finance and engineering teams on finance processes. The position is onsite in Bangkok, Thailand.
From the employer
The Senior Machine Learning Engineer I sits in the Finance Transformation team in IT at CP Axtra, building and operating AI/LLM-powered features that automate and augment finance processes across Makro and Lotus's. The role requires strong engineering fundamentals, hands-on experience deploying AI features into production, and the ability to work with Finance and cross-functional teams. It does not focus on developing custom ML models, but deep technical proficiency and system-level thinking are essential.
Responsibilities:
AI and LLM Evaluation
•Evaluate large language models and AI services for accuracy, reliability, safety, latency, and business suitability on finance use cases.
•Design structured evaluation frameworks, test cases, and benchmarking methodologies.
•Conduct prompt testing, retrieval validation, and failure-mode analysis.
•Implement quality guardrails, safety filters, and monitoring for LLM applications handling sensitive financial data.
System and Backend Development
•Build and maintain backend services and APIs that integrate LLMs or AI workflows with finance systems such as Oracle Fusion.
•Architect scalable systems supporting chat interfaces, retrieval pipelines, classification tools, or finance workflow automation.
•Implement solid software engineering practices: testing, versioning, error handling, observability, and performance optimization.
•Ensure robust integration with internal systems, data services, and production infrastructure.
AI Application Engineering
•Work on features powered by LLMs such as RAG systems, finance copilots, document intelligence for invoices/contracts, and intelligent automation.
•Implement embeddings, document retrieval layers, vector search, caching, and fallback logic.
•Collaborate with platform teams on deployment, API management, and resource optimization.
Operations and Reliability
•Monitor AI features in production and proactively address model drift, latency issues, and failure patterns.
•Maintain evaluation logs, experiment results, and version control for AI workflows.
•Work with DevOps to manage CI/CD pipelines, container deployment, and runtime environments.
Collaboration and Delivery
•Partner with product owners, Finance teams, and engineering teams to convert requirements into reliable AI solutions.
•Provide technical guidance on feasibility, architecture choices, and operational trade-offs.
•Produce clear documentation on workflows, system design, evaluation methods, and application behavior.
Requirements
1. 5 to 8 years of experience in software engineering, ML engineering, or AI engineering.
2. Strong proficiency in Python and experience designing production-grade backend services.
3. Proven experience deploying AI or LLM-based applications into production environments.
4. Solid understanding of system design, distributed systems, APIs, and microservices.
5. Experience with LLM tooling such as Azure OpenAI, ChatGPT, or similar platforms.
6. Hands-on experience with vector databases, embeddings, or retrieval-based architectures.
7. Strong problem-solving skills and the ability to evaluate AI model behavior systematically.
8. Experience with Docker, Kubernetes, CI/CD pipelines, and cloud environments.
Preferred
1. Experience building or maintaining RAG systems, chat systems, or AI automation workflows.
2. Familiarity with observability tools (logging, tracing, monitoring) in production environments.
3. Experience working with Airflow, Prefect, or orchestration frameworks.
4. Knowledge of data pipelines, ETL workflows, or integration with ERP systems such as Oracle Fusion.
5. Domain knowledge in finance, retail, loyalty, process automation, or enterprise systems.
Benefits
- International workplace
- Opportunities for growth in e-commerce, wholesales, and retail industry
- Competitive benefits
- Fast-paced, dynamic, and supportive environment