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Open nowPosted 2 days ago

AI/ML Engineer (Agentic AI & Data Science) – 12-Month Contract | Banking Client | MBFC

MyCareersFuture93,320 open roles

Pay
SGD 5,000 – SGD 7,500 a month
Where
Central, Singapore
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Your applicationOpen nowAI/ML Engineer (Agentic AI & Data Science) – 12-Month Contract | Banking Client | MBFCMyCareersFuture · Central, Singapore

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  2. 3.9%3 days
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  4. 15.5%14 days
  5. 34.0%30 days
This job: posted 2 days ago

MyCareersFuture median: 7 days open

The posting

AI/ML Engineer (Agentic AI & Data Science) – 12-Month Contract | Banking Client | MBFC

Employer: NTT Singapore Pte. Ltd. Employment Type: 12-month contract, renewable subject to performance and business requirements. Work Location: MBFC , Singapore Client Industry: Banking / Financial Services

Please apply only if you are comfortable with a 12-month contract, renewable subject to performance and business requirements. based in MBFC

About the Role

We are seeking an AI/ML Engineer with strong Data Science and Generative AI capabilities to design, develop and deploy AI solutions supporting a banking client’s Global Financial Markets business.

The role covers Agentic AI platforms, recommendation engines, retrieval systems and predictive models for market intelligence, product recommendations, client engagement, workflow automation and knowledge management.

You will collaborate with business stakeholders, product owners, data scientists and software engineers to deliver scalable, production-ready AI solutions.

Key Responsibilities

Generative AI and Agentic AI

  • Design and develop AI agents using Dify and modern Agentic AI frameworks.
  • Build and optimise RAG solutions, including document ingestion, embeddings, vector search, retrieval and reranking.
  • Develop agent orchestration workflows and integrate tools, APIs and enterprise data sources.
  • Implement prompt engineering, evaluation, reflection, memory and state management.
  • Develop reusable AI components for multiple business use cases.

Machine Learning and Data Science

  • Develop models for client propensity prediction, recommendations, classification, ranking, behavioural analytics and next-best-action recommendations.
  • Perform exploratory data analysis, feature engineering, model training and evaluation.
  • Analyse structured and unstructured datasets to generate actionable business insights.
  • Monitor model performance and improve accuracy, relevance and reliability.

Application Development and Deployment

  • Build production-ready AI services and REST APIs using Python.
  • Integrate AI applications with enterprise systems and knowledge repositories.
  • Develop data pipelines, transformations and data quality controls.
  • Deploy and operate AI services using OpenShift/OCP, Docker and Kubernetes.
  • Implement CI/CD pipelines, monitoring, observability and evaluation frameworks.
  • Optimise performance, scalability and infrastructure costs.
  • Maintain technical documentation and collaborate with stakeholders throughout delivery.

Required Qualifications and Experience

  • Relevant experience in AI Engineering, Machine Learning Engineering, Data Science or Advanced Analytics.
  • Hands-on experience building and deploying AI or ML solutions into production.
  • Strong Python programming skills, with practical SQL and REST API experience.
  • Experience with Scikit-learn, XGBoost/LightGBM, and TensorFlow or PyTorch.
  • Hands-on experience in all five mandatory Generative AI areas: RAG, vector search, LLM applications, Dify and Agentic AI frameworks.
  • Knowledge of data pipelines, data transformation, feature engineering and data quality management.
  • Experience with OpenShift/OCP, Docker, Kubernetes, CI/CD pipelines and Git.
  • Ability to translate business requirements into technical solutions and communicate clearly with business and engineering teams.

Preferred Experience

  • Banking, financial services, capital markets or wealth management.
  • Recommendation engines, personalisation and next-best-action solutions.
  • Search, retrieval and enterprise knowledge management platforms.
  • MLOps, LLMOps and AI governance.
  • Unstructured document repositories and enterprise knowledge sources.

Interested candidates are kindly requested to email their CV with their experience to [email protected]

We look forward to your application!

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