AI Engineer- CONTRACT- 5K$- Insurer- NO VISA SPONSORSHIP

Argyll Scott; Malaysia; On-site; MYR 120,000 - 240,000; Full time

Job summary

Argyll Scott is actively seeking an experienced AI Engineer to design, develop, and deploy enterprise AI solutions across the insurance domain.

The role focuses on practical AI and Generative AI applications that support underwriting, claims, customer service, distribution, risk management, and operational efficiency. You will collaborate with data engineers, data scientists, and business stakeholders to deliver scalable AI services and robust production-grade systems.

Qualifications

• Bachelor's or Master's degree in Computer Science, AI, Data Science, Engineering.
• 5+ years of software/AI engineering experience.
• Hands-on AI/ML or Generative AI experience.
• Experience with LangChain, LangGraph, Hugging Face, TensorFlow, PyTorch.
• Proficiency in Python and scalable API development.
• Experience with AWS, Azure, or GCP and MLOps practices.
• Strong problem solving and communication skills.
• Ability to collaborate with technical and non-technical stakeholders.

Responsibilities

• Design, develop, and deploy AI and Generative AI solutions for insurance use cases.
• Build AI apps with LLMs, RAG, AI agents, and predictive analytics.
• Develop AI capabilities for underwriting, claims, fraud detection, and customer service.
• Integrate AI services with enterprise data platforms and systems.
• Develop RAG pipelines using embeddings, vector databases, and semantic search.
• Create agentic workflows using LangChain and LangGraph.
• Work with LLM platforms like GPT, Claude, Gemini.
• Deploy scalable AI applications on cloud platforms (AWS/Azure/GCP).
• Implement MLOps and GenAIOps for deployment, monitoring, testing.
• Ensure security, governance, and responsible AI practices.

Skills

AI/ML

Generative AI

LLMs

Python

APIs

MLOps

Cloud platforms

Data engineering

Software development

Education

Bachelor's or Master's in CS/AI/Data Science/Engineering

Tools

LangChain

LangGraph

Hugging Face

TensorFlow

PyTorch

Docker

Kubernetes

Job description

Role Overview

We are seeking an experienced AI Engineer to design, develop, and implement enterprise AI solutions across the insurance business. This role will focus on building and deploying practical AI and Generative AI applications that support underwriting, claims, customer service, distribution, risk management, and operational efficiency initiatives.

The ideal candidate will have strong hands-on experience in AI/ML, Generative AI, LLMs, cloud platforms, data engineering, and software development, with the ability to translate business requirements into scalable and production-ready AI solutions.

Key Responsibilities

• Design, develop, and deploy AI and Generative AI solutions for insurance use cases.
• Build applications using LLMs, RAG, AI agents, machine learning, and predictive analytics.
• Develop AI solutions supporting underwriting, claims processing, fraud detection, customer engagement, and insurance operations.
• Build and integrate AI services, APIs, and applications with enterprise systems and data platforms.
• Develop RAG pipelines using embeddings, vector databases, semantic search, and enterprise knowledge sources.
• Develop and integrate AI agents and agentic workflows using frameworks such as LangChain and LangGraph.
• Work with LLM platforms and models including GPT, Claude, Gemini, and other emerging AI technologies.
• Develop scalable AI applications using Python and modern software engineering practices.
• Deploy AI solutions on cloud platforms such as AWS, Azure, or GCP.
• Implement MLOps and GenAIOps practices for model deployment, monitoring, testing, and continuous improvement.
• Integrate AI solutions with core insurance systems, CRM platforms, data warehouses, policy administration systems, and external data providers.
• Work with data engineers and data scientists to prepare, process, and utilise structured and unstructured data for AI applications.
• Implement appropriate security, governance, monitoring, and responsible AI practices within AI solutions.
• Troubleshoot, optimise, and improve the performance, scalability, reliability, and accuracy of AI applications.
• Evaluate emerging AI technologies and frameworks and identify opportunities to apply them to business problems.
• Collaborate with product managers, business stakeholders, architects, data scientists, and engineering teams to deliver AI solutions.
• Contribute to technical documentation, coding standards, testing practices, and AI engineering best practices.

Required Qualifications

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related discipline.
• 5+ years of experience in software engineering, AI engineering, machine learning, or related technical roles.
• Strong hands-on experience developing and implementing AI/ML or Generative AI solutions.
• Practical experience with Generative AI, Large Language Models (LLMs), RAG, and AI agents.
• Strong programming skills in Python and experience developing scalable APIs and AI services.
• Hands-on experience with AI/ML frameworks such as LangChain, LangGraph, Hugging Face, TensorFlow, PyTorch, or similar technologies.
• Experience working with LLM APIs, prompt engineering, embeddings, vector databases, and semantic search.
• Experience with cloud platforms such as AWS, Azure, or GCP.
• Strong understanding of software engineering principles, APIs, microservices, CI/CD, and version control.
• Experience with Docker, Kubernetes, DevOps, and MLOps practices.
• Experience working with structured and unstructured data and modern data platforms.
• Strong problem-solving, analytical, and communication skills.
• Ability to work collaboratively with both technical and non-technical stakeholders.

Preferred Qualifications

• Experience working within insurance, financial services, or other regulated industries.
• Knowledge of insurance processes such as underwriting, claims, policy administration, distribution, customer service, or actuarial processes.
• Experience building production-grade RAG and agentic AI applications.
• Experience with AI evaluation, model monitoring, prompt evaluation, and LLM observability.
• Understanding of AI security, responsible AI, data privacy, and regulatory requirements.
• Experience with enterprise knowledge management and document processing solutions.
• Experience integrating AI applications with enterprise platforms and third-party APIs.
• Exposure to autonomous AI workflows and enterprise AI orchestration platforms.
• Relevant certifications in cloud, AI, machine learning, or software engineering.