AI/ML Engineer (Agentic AI & Data Science) #IAC

Recruit Express Pte Ltd; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

Recruit Express Pte Ltd seeks a hands-on AI engineer to turn business requirements into scalable AI architectures and RAG components. You will build predictive models, deploy Python services, and optimize end-to-end data workflows.

You will work with Docker, Kubernetes and OpenShift to maintain robust deployments and collaborate across engineering, product and security teams.

Qualifications

• Strong Python and SQL skills with hands-on implementation.
• Practical experience with Scikit-learn, XGBoost or LightGBM, and TF or PyTorch.
• Hands-on development and deployment of predictive ML models.
• Understanding of classification/ regression metrics and validation.
• Experience with data pipelines, feature engineering and data quality controls.
• Experience with Docker, Kubernetes, OpenShift and CI/CD pipelines.
• Ability to explain contributions, trade-offs, results and production issues clearly.

Responsibilities

• Translate business requirements into AI architectures, plans and evaluation criteria.
• Develop reusable agents and RAG components for market intelligence and automation.
• Build predictive models for propensity, ranking, and recommendations.
• Perform data analysis, feature engineering, and model training with tuning.
• Deploy Python AI services and REST APIs integrating enterprise data.
• Investigate production issues across retrieval, model inference and APIs.
• Collaborate with business, product, data, engineering, security teams.

Skills

Python

SQL

Scikit-learn

XGBoost

TensorFlow

PyTorch

Docker

Kubernetes

OpenShift

LangGraph

LangChain

AutoGen

CrewAI

Semantic Kernel

Tools

LangGraph

LangChain

AutoGen

CrewAI

Semantic Kernel

Job description

Responsibilities

• Translate business requirements into AI architectures, implementation plans and measurable evaluation criteria.
• Develop reusable agents and RAG components for market intelligence, client engagement, workflow automation and enterprise knowledge management.
• Build predictive models for client propensity, classification, ranking, behavioural analytics, recommendations and next-best-action use cases.
• Perform exploratory data analysis, feature engineering, model training, hyperparameter tuning and evaluation.
• Establish suitable baselines and validation strategies, prevent data leakage and assess model generalisation.
• Build Python-based AI services and REST APIs, integrating structured and unstructured enterprise data.
• Deploy and support applications using Docker, Kubernetes and Red Hat OpenShift/OCP.
• Implement CI/CD, automated testing, model/application monitoring and operational documentation.
• Investigate production failures across retrieval, agent execution, model inference, APIs and infrastructure.
• Optimise accuracy, relevance, latency, reliability and operating cost.
• Collaborate with business, product, data, engineering, infrastructure and security teams.
• Any other ad-hoc duties as assigned by supervisors

Mandatory Generative AI Requirements

Hands-on experience in all five areas below is required:

1. RAG: Implementing document ingestion, parsing, chunking, embeddings, retrieval, reranking and grounded response generation.
2. Vector Search: Implementing vector indexing, similarity search, metadata filtering and retrieval-quality evaluation.
3. LLM Applications: Building applications integrated with enterprise data sources and APIs, including output validation, error handling and monitoring.
4. Dify: Building LLM applications or agent workflows, configuring knowledge bases, integrating tools and publishing applications through APIs.
5. Agentic AI Frameworks: Implementing workflows using frameworks such as LangGraph, LangChain, AutoGen, CrewAI or Semantic Kernel, including tool calling, state management, memory and controlled execution.

Requirements

• Strong Python and SQL, with the ability to implement and troubleshoot solutions directly.
• Practical experience with Scikit-learn, XGBoost or LightGBM, and TensorFlow or PyTorch.
• Hands-on development and deployment of predictive ML models, beyond integrating pre-trained model APIs.
• Understanding of classification and regression metrics, class imbalance, overfitting, cross-validation and appropriate time-based validation.
• Experience with data pipelines, transformation, feature engineering and data quality controls.
• Experience with Docker, Kubernetes, OpenShift/OCP, Git and CI/CD pipelines.
• Experience evaluating retrieval quality and LLM outputs, including groundedness, relevance and failure cases.
• Ability to explain personal contributions, technical trade-offs, evaluation results and production issues clearly.

• Banking, financial services, capital markets or wealth management.
• Recommendation engines, personalisation, propensity modelling or ranking.
• Enterprise search and document repositories.
• MLOps, LLMOps, model monitoring and AI governance.