AI ML Engineer

TeamLease Digital Global; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

TeamLease Digital Global seeks an AI/ML Engineer for a 12-month banking client project. The role emphasizes Generative AI, agentic AI, RAG, vector search and production deployment, collaborating with data scientists and software engineers to translate business needs into scalable AI solutions.

You will build agentic AI platforms, retrieval systems, recommendation engines and predictive models supporting market intelligence and client engagement, with strong emphasis on production readiness and

Qualifications

• Relevant experience in AI Engineering, ML Engineering, Data Science or Advanced Analytics.
• Hands-on experience deploying AI/ML solutions to production.
• Strong Python programming and API skills.
• Experience with Scikit-learn, XGBoost/LightGBM and TF or PyTorch.
• Knowledge of data pipelines, feature engineering and data quality management.
• Experience with OpenShift/OCP, Docker, Kubernetes, CI/CD and Git.
• Ability to translate business needs into technical solutions.

Responsibilities

• Design and build AI agents and agentic AI solutions.
• Develop RAG and vector search capabilities for enterprise data.
• Create deployment workflows, tool integrations and memory components.
• Develop production AI services and REST APIs in Python.
• Implement CI/CD, monitoring and evaluation frameworks.
• Collaborate with stakeholders to deliver scalable AI products.

Skills

Python programming

SQL

REST APIs

Scikit-learn

XGBoost

LightGBM

TensorFlow

PyTorch

OpenShift

Docker

Kubernetes

CI/CD

Git

RAG

Vector search

Dify

Agentic AI

Tools

OpenShift

Docker

Kubernetes

Job description

AI/ML Engineer – Agentic AI & Data Science

Experience: 3 + years to 10 years

Client: Banking client

Employment Type: 12-month contract

About the Role

We are seeking an AI/ML Engineer with strong Data Science and Generative AI capabilities to develop and deliver AI solutions supporting Global Financial Markets.

You will build Agentic AI platforms, retrieval systems, recommendation engines 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 translate business requirements into scalable, production-ready AI solutions.

Key Responsibilities

Generative AI & Agentic AI

• Design and develop AI agents using modern agent frameworks and Dify.
• Build and optimise Retrieval-Augmented Generation (RAG) solutions and vector search capabilities.
• Develop agent orchestration workflows, tool integrations and tool-calling capabilities.
• Implement prompt engineering, evaluation, reflection and memory capabilities.
• Build reusable AI components for multiple business use cases.
• Develop models for client propensity prediction, recommendation systems, 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.

AI Application Development & Deployment

• Build production-ready AI services and REST APIs using Python.
• Integrate AI solutions with enterprise applications, data sources and document repositories.
• Develop data pipelines and transformations, with appropriate data quality controls.
• Deploy and operate AI applications using OpenShift/OCP, Docker and Kubernetes.
• Implement CI/CD pipelines, monitoring, observability and evaluation frameworks.
• Optimise solutions for performance, scalability and cost efficiency.

Required Qualifications & Skills

• 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 experience in SQL and REST APIs.
• Experience with Scikit-learn, XGBoost/LightGBM and TensorFlow or PyTorch.
• 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 understand business requirements and communicate technical solutions clearly.

Mandatory Generative AI experience in all the following areas:

• Retrieval-Augmented Generation (RAG)
• Vector search
• Dify
• Agentic AI frameworks

Preferred Qualifications

• Experience in financial services, banking, capital markets or wealth management.
• Experience building recommendation engines, personalisation or next-best-action solutions.
• Experience with search, retrieval and knowledge management platforms.
• Familiarity with MLOps, LLMOps and AI governance practices.
• Experience working with unstructured document repositories and enterprise knowledge sources.