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
- 5 to 8 years of experience in software engineering, ML engineering, or AI engineering.
- Strong proficiency in Python and experience designing production-grade backend services.
- Proven experience deploying AI or LLM-based applications into production environments.
- Solid understanding of system design, distributed systems, APIs, and microservices.
- Experience with LLM tooling such as Azure OpenAI, ChatGPT, or similar platforms.
- Hands-on experience with vector databases, embeddings, or retrieval-based architectures.
- Strong problem-solving skills and the ability to evaluate AI model behavior systematically.
- Experience with Docker, Kubernetes, CI/CD pipelines, and cloud environments.
Preferred
- Experience building or maintaining RAG systems, chat systems, or AI automation workflows.
- Familiarity with observability tools (logging, tracing, monitoring) in production environments.
- Experience working with Airflow, Prefect, or orchestration frameworks.
- Knowledge of data pipelines, ETL workflows, or integration with ERP systems such as Oracle Fusion.
- 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
