AI Engineer
Capco is hiring an AI Engineer in Singapore, Singapore. Level rates it ; you can apply on Capco.
Level's read on this job
- On Level's scale, this AI engineer role is Builds AI ●●●●: the job is building AI systems.
- Tools and skills the posting cites include LangChain, LangGraph and RAG.
- Level classifies it as a mid-level role.
- Level lists 702 other open roles at Capco.
AI in this role
Capco
Capco is a leading global technology and management consultancy that excels in driving digital transformation within the financial services and insurance sectors. With an unwavering commitment to delivering practical solutions, we empower our clients to navigate the complexities of today's fast-paced market. Our integrated services span across major financial hubs worldwide, ensuring that we are always close to our clients and their needs.
At Capco, we possess not only the expertise but also the vision and passion necessary to lead transformative change. As we embark on an exciting growth journey, this is the perfect moment to join our team. We are expanding geographically, increasing our workforce, and poised to disrupt the consulting landscape across APAC with our entrepreneurial spirit and agile methodologies.
About the Role
We are thrilled to introduce an opportunity for a talented AI Engineer to join our Singapore team. This role is designed for a technology innovator who thrives on solving complex challenges and is passionate about building AI-driven solutions that transform the Financial Services industry. As an AI Engineer at Capco, you will play a pivotal role in shaping the future of insurance by leveraging cutting-edge AI technologies to deliver intelligent, scalable, and compliant solutions. Your work will enable our clients to unlock new efficiencies, enhance customer experience, and stay ahead in a rapidly evolving digital landscape. If you are excited about applying advanced AI techniques to real-world business problems and driving measurable impact, we want you on our team.
Key Responsibilities
- Build multi-agent workflows to optimize business processes within insurance.
- Develop and maintain software pipelines for data processing, model training, and deployment using Python-based frameworks.
- Train, fine-tune, and optimize neural network models, including LLMs, for specific use cases such as risk assessment and customer query resolution.
- Collaborate with data scientists, engineers, and business stakeholders to translate requirements into production-ready AI applications.
- Ensure AI models adhere to banking regulations, data privacy standards (e.g., GDPR, CCPA), and ethical AI practices.
- Monitor deployed solutions in production, troubleshooting issues, and implement updates as needed.
- Strong experience delivering production-grade applications, backend services and technical components that support data, workflow, automation or AI-driven solutions.
- Participate in code reviews, agile ceremonies, and CI/CD processes.
Experience and Qualifications
- 3–12 years of professional experience in AI/ML engineering, with a focus on software development and deployment.
- Build and integrate components for LLM, RAG, search, agent-based and automation solutions,
- Strong software engineering experience with Python, Java, React Next, Node.js
- Hands-on experience training and fine-tuning neural network models, with a track record of optimizing for performance and scalability.
- Proven expertise working with LLMs (e.g., GPT, BERT) and building agentic solutions using frameworks like LangChain, LangGraph, or ADK.
- Familiarity with cloud platforms (AWS, Azure, or Google Cloud) for model deployment and data management.
- Solid understanding of machine learning concepts, including supervised/unsupervised learning, evaluation metrics, and bias mitigation.
- Experience with version control systems (e.g., Git) and agile methodologies.
- Excellent problem-solving skills and ability to work in a fast-paced, collaborative environment.
Preferred Qualifications
- Experience in financial services domains
- Familiarity with big data tools (e.g., Spark, Hadoop) and databases (SQL/NoSQL).
- Knowledge of graph databases and knowledge graphs.
- Understanding DevOps practices, containerization (Docker, Kubernetes), and MLOps workflows.
- Publications, open-source contributions, or certifications in AI/ML.
- Strong communication skills for presenting technical concepts to non-technical stakeholders.
ELIGIBILITY TO WORK IN SG
Applicants must have unrestricted working rights in Singapore. Only Singapore Citizens and Singapore Permanent Residents (PRs) will be considered. Employment Pass sponsorship is not available for this role.
How we rate this
AI Engineer at Capco rates 97 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Also common in AI engineer jobs at this level
Pay
- Typical AI engineer job
- $184k
This posting doesn't list pay. Use the typical AI engineer figure when pay comes up. See AI engineer jobs
Questions you could be asked
- How would you design a retrieval step so the model answers from real data instead of guessing?
- Walk me through fine-tuning a model: what data did you use, and how did you check the result?
- How do you monitor a model once it's live, and how do you know it needs retraining?
- What's a project where you used LangChain hands-on?
- Walk me through how you've used LangGraph in your day-to-day work.
Adapt your resume
- List these exact terms on your resume: RAG, Fine-tuning, ML Ops, LangChain, and LangGraph. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.
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