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

ResponsibilitiesTranslate 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 RequirementsHands-on experience in all five areas below is required:RAG: Implementing document ingestion, parsing, chunking, embeddings, retrieval, reranking and grounded response generation.Vector Search: Implementing vector indexing, similarity search, metadata filtering and retrieval-quality evaluation.LLM Applications: Building applications integrated with enterprise data sources and APIs, including output validation, error handling and monitoring.Dify: Building LLM applications or agent workflows, configuring knowledge bases, integrating tools and publishing applications through APIs.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.RequirementsStrong 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.Interested applicants, please email your resume to Andre Chua Jing MingEmail: [email protected] Reg No: R1989053EA Licence No: 99C4599Recruit Express Pte Ltd