Senior Machine Learning Engineer
At a glance
Senior Machine Learning Engineering role at Kredivo Group. Jakarta · full-time.
Pay not stated Machine Learning Engineer salary
aimlroles summary, based on the employer's posting.
What you'll do
- Make architecture choices for your services, balancing correctness, scale and delivery speed
- Improve code quality through hands-on work, careful reviews and consistent engineering practices
- Coach engineers, shape technical direction and build agreement across teams
- Lead or contribute substantially to AI systems such as chat, RAG, OCR and agentic workflows
- Use SQL and Python to investigate production issues and support engineering and product decisions
What you bring
- You have led system design, maintained strong code quality and delivered complex projects end to end
- You have mentored engineers, set technical direction and influenced decisions across teams
- You communicate clearly enough to align engineering, product and business stakeholders
- You bring solid foundations in algorithms, data structures, distributed systems and API or service design
Who this fits
This senior role suits an engineer who pairs hands-on technical ownership with mentoring and cross-team influence. You’ll help direct work beyond your immediate team while supporting reliable production systems. AI, LLM and agentic systems experience is especially relevant; Python, SQL, high-availability systems and OCR are listed as preferred qualifications.
From the employer
We are hiring a Senior Machine Learning Engineer (SDE3) at Kredivo. This role carries meaningfully more leadership weight than a typical SDE3: beyond individual technical ownership, the successful candidate will mentor engineers, set technical direction within their domain, and influence delivery across teams. We are especially interested in candidates who can bring AI/LLM and agentic systems expertise to bear on real production problems (e.g. chatbots, OCR pipelines, RAG-based tooling) within Kredivo's high-availability fintech environment.
About the Job:
- Own system design and architecture decisions for services within your domain, balancing correctness, scalability, and delivery speed.
- Drive code quality through hands-on contribution, thoughtful code review, and enforcement of engineering best practices.
- Mentor and coach engineers at all levels, actively developing the technical skills of the broader team.
- Set technical direction for your area and build consensus across engineering, product, and adjacent teams.
- Represent engineering in cross-team initiatives, influencing technical decisions beyond your immediate team's scope.
- Lead or significantly contribute to the design and delivery of AI-powered systems (LLM-based chat, RAG pipelines, OCR/document processing, agentic workflows).
- Ensure the reliability, availability, and throughput of production systems that operate at fintech scale.
- Use data analysis (SQL, Python) to inform engineering and product decisions, including debugging and root-causing production issues.
Required Qualifications:
- Track record of leading system design, maintaining high code quality, and reliably delivering complex projects end-to-end.
- Strong leadership skills: demonstrated experience mentoring engineers, setting technical direction, and influencing outcomes across teams.
- Excellent communication skills, able to align stakeholders across engineering, product, and business functions.
- Solid computer science fundamentals: data structures, algorithms, distributed systems, and API/service design.
- Preferred Qualifications
- Hands-on exposure to AI/RAG/LLM systems and agentic architectures (e.g. building or operating chatbots, retrieval pipelines, tool-using agents).
- Experience designing and operating high-availability, high-throughput production systems, ideally in fintech or another regulated, latency-sensitive domain.
- Strong proficiency in Python, including for backend services, data pipelines, or AI/ML tooling.
- Strong SQL skills for data analysis. Comfortable querying, profiling, and drawing conclusions from large operational datasets.
- Experience with OCR pipelines or document-processing systems is a plus.