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Capco

AI & ML Engineer | Banking

Capco is hiring an AI & ML Engineer | Banking in Hong Kong, Hong Kong SAR China. Level rates it ; you can apply on Capco.

Level's read on this job

  • Level rates this machine learning engineer role Builds AI ●●●●: the job is building AI systems.
  • Tools and skills the posting cites include LangChain, LangGraph and prompt engineering.
  • Level lists 691 other open roles at Capco.
  • The role was posted in the last few days.

AI in this role

LangChainLangGraph
prompt-engineeringragfine-tuningml-opsai-evaluationnlpai-safety

Location: Hong Kong | Practice Area: Technology & Engineering | Type: Permanent

Build intelligent solutions. Shape the future of AI in financial services.

The Role

At Capco, a Wipro company, we're transforming financial services through technology, innovation and deep industry expertise. We work with leading financial institutions to solve complex challenges and deliver meaningful change.

We're looking for an AI & ML Engineer to join our Hong Kong team, developing innovative, scalable AI solutions that deliver real business value.

You'll work closely with business stakeholders, product owners and engineering teams to prototype, build and deploy AI-powered applications. This hands-on role focuses on AI, including Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Agentic AI and advanced machine learning.

You'll also champion responsible AI-enabled engineering practices, using approved AI tools, human oversight and robust evaluation frameworks to deliver secure, reliable and production-ready solutions.

What You’ll Do

  • Design & Deliver AI Solutions: Develop, prototype and deploy scalable AI and machine learning applications that address real business challenges, using approved AI-enabled workflows to improve delivery speed and quality.
  • Build Intelligent AI Applications: Develop AI solutions, including LLMs, RAG and Agentic AI workflows, incorporating prompt engineering, retrieval optimisation, tool integration and automation.
  • Optimise AI Performance: Select, develop and fine-tune machine learning models to improve accuracy, efficiency, performance and cost-effectiveness.
  • Embed Responsible AI: Build evaluation frameworks, guardrails and monitoring capabilities to manage AI reliability, hallucinations, security and data privacy, incorporating human-in-the-loop review, auditability and appropriate escalation pathways.
  • Collaborate & Scale: Partner with business, product, data science and engineering teams to translate requirements into practical solutions, supporting deployment, MLOps, continuous improvement and reusable AI components.

What We’re Looking For

  • AI & Engineering Experience: Relevant experience in AI/ML engineering, software engineering or related technical roles, with practical experience delivering AI-powered solutions.
  • Python & Software Development: Hands-on Python programming expertise, supported by sound software engineering principles and experience building production-grade applications, APIs and AI/ML pipelines.
  • Applied AI Expertise: Practical experience with LLMs, Generative AI, RAG and Agentic AI frameworks such as LangChain or LangGraph, alongside knowledge of machine learning, model selection, fine-tuning and evaluation.
  • Cloud & Responsible AI: Familiarity with AWS, Azure or Google Cloud, alongside an understanding of AI security, data privacy, responsible AI and production deployment practices, including appropriate human oversight and model validation.
  • Collaboration & Problem-Solving: Effective communication, analytical thinking and stakeholder engagement skills, with a willingness to explore approved AI tools and emerging techniques to improve engineering outcomes.

Bonus Points For

  • Experience delivering AI and machine learning solutions within banking, financial services or other regulated environments.
  • Familiarity with MLOps, CI/CD, Docker, Kubernetes and model monitoring.
  • Knowledge of advanced AI techniques, including natural language processing, deep learning, graph machine learning or multimodal AI.
  • Experience developing AI evaluation frameworks, performance optimisation approaches and reusable engineering components.
  • Exposure to AI governance, automated evaluation, human-in-the-loop controls and responsible deployment of Agentic AI solutions.

Why Join Capco

  • Deliver high-impact technology solutions for Tier 1 financial institutions.
  • Work in a collaborative, flat, and entrepreneurial consulting culture.
  • Access continuous learning, training, and industry certifications.
  • Be part of a team shaping the future of digital financial services.
  • Help shape the future of digital transformation across FS & Energy.

Inclusion at Capco

We're committed to making our recruitment process accessible and straightforward for everyone. If you need any adjustments at any stage, just let us know – we'll be happy to help.

We value each person's unique perspective and contribution. At Capco, we believe that being yourself is your greatest strength. Our #BeYourselfAtWork culture encourages individuality and collaboration – a mindset that shapes how we work with clients and each other every day.

How we rate this

AI & ML Engineer | Banking 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.

Classification

Builds AI. The job is building AI systems.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ 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

Prompt engineeringRAGFine-tuningML OpsAI EvaluationNLPAI SafetyLangChain

Also common in machine learning engineer jobs at this level

Pay

Typical machine learning engineer job
$198k

This posting doesn't list pay. Use the typical machine learning engineer figure when pay comes up. See machine learning engineer jobs

Questions you could be asked

  1. How do you structure and test a prompt to get consistent output from a language model?
  2. How would you design a retrieval step so the model answers from real data instead of guessing?
  3. Walk me through fine-tuning a model: what data did you use, and how did you check the result?
  4. How do you monitor a model once it's live, and how do you know it needs retraining?
  5. How do you decide that one model's output is better than another's for a given task?

Adapt your resume

  • List these exact terms on your resume: Prompt engineering, RAG, Fine-tuning, ML Ops, and AI Evaluation. 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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