Senior Data Scientist - Gen AI

TANGSPAC SEARCH PRIVATE LIMITED; Singapore; On-site; SGD 180,000 - 260,000; Full time

Job summary

Tangspac Search Private Limited is seeking a Data Scientist – Generative AI to design, develop, and deploy enterprise-grade AI applications. You will bridge hands-on technical work with solution delivery, turning GenAI research into robust, production-ready systems.

You will collaborate with product owners, software engineers, and business stakeholders to drive technical excellence in GenAI, operationalize scalable ML pipelines, and ensure safe, ethical AI deployment across the organization.

Qualifications

• 8+ years in Data Science / ML with production-grade AI apps.
• Banking experience is a plus.
• GenAI expertise: LangChain, LlamaIndex, RAG, Agentic AI patterns.
• Strong Python/SQL skills; deep learning with PyTorch & TensorFlow.
• Experience with cloud ML platforms and vector databases.
• Proven delivery in Agile environments.

Responsibilities

• Lead design, fine-tuning, deployment of Generative AI frameworks (RAG, vector DBs, Agentic AI).
• Oversee end-to-end ML lifecycle: feature engineering, data pipelines, MLOps/LLMOps, monitoring, tuning.
• Develop NLP, DL, and predictive models to solve business problems and optimize workflows.
• Translate business needs into data science roadmaps and manage milestones.
• Implement enterprise evaluation frameworks for accuracy, hallucination, toxicity, safety.
• Operationalize AI models via secure cloud architectures, microservices, APIs, CI/CD.
• Collaborate with PMs, engineers, and business units to embed AI into platforms.
• Communicate model outcomes and ROI to non-technical leadership.

Skills

Python

SQL

PyTorch

TensorFlow

MLOps

Agile

Education

Master's or PhD in Computer Science/Data Science/Statistics/Mathematics

Tools

LangChain

LlamaIndex

Pinecone

Weaviate

Qdrant

SageMaker

Vertex AI

LLMOps

Job description

A Leading International bank is seeking a highly skilled and execution-focused Data Scientist – Generative AI to carry out design, development, and deployment of enterprise-grade AI applications. In this role, you will sit at the intersection of hands-on technical architecture and solution delivery—turning innovative AI/GenAI research into robust, production-ready systems.

You will collaborate directly with product owners, software engineers, and cross-functional business stakeholders. Your mission will be to drive technical excellence in Generative AI, operationalize scalable machine learning pipelines, and ensure that AI models are deployed safely, ethically, and effectively across the organization.

Key Responsibilities

1. AI Model Architecture & Technical Execution

1. Lead the design, fine-tuning, and deployment of Generative AI frameworks, including Retrieval-Augmented Generation (RAG), Vector Databases, and Agentic AI workflows.
2. Oversee the end-to-end Machine Learning lifecycle—from feature engineering and data pipelines to production MLOps/LLMOps automation, continuous monitoring, and performance tuning.
3. Develop custom NLP, Deep Learning, and predictive models to solve complex business problems, optimize operational workflows, and enhance user experiences.
4. Translate business requirements into actionable data science roadmaps, managing project delivery timelines, technical milestones, and code quality.
5. Implement enterprise-grade evaluation frameworks to assess GenAI model outputs for accuracy, hallucination rates, toxicity, and safety.
6. Operationalize AI models through secure cloud architectures (AWS/Azure/GCP), microservices, APIs, and CI/CD pipelines to ensure low latency and high scalability.
7. Work closely with Product Managers, Software Engineers, and Business Units to embed AI capabilities seamlessly into core platforms and daily workflows.
8. Clearly communicate complex technical outcomes, model performance metrics, and project ROI to non-technical stakeholders and business leadership.

Key Requirements & Qualifications

1. Education: Master’s degree or Ph.D. in Computer Science, Data Science, Statistics, Mathematics, or a related quantitative field.
2. Min 8 years of professional experience in Data Science / Machine Learning, with demonstrated experience delivering production-grade AI applications.
3. Prior experience working in banking is a big plus
4. Generative AI Expertise: Proven hands-on command of LLM frameworks (e.g., LangChain, LlamaIndex), Prompt Engineering, RAG architectures, and Agentic AI patterns.
5. Technical Stack Mastery:
6. Expert programming skills in Python and SQL .
7. Proficiency with deep learning frameworks (PyTorch, TensorFlow )
8. Hands-on experience with cloud-native ML platforms (AWS SageMaker, Azure ML, or GCP Vertex AI), Vector DBs (Pinecone, Weaviate, Qdrant), and MLOps/LLMOps tooling.
9. Project Execution: Strong track record of technical delivery, problem-solving, and cross-functional execution in an Agile environment.