AI Engineering, Specialist

At AIA we’ve started an exciting movement to create a healthier, more sustainable future for everyone.

As pioneering innovators for over 100 years, we’re now transforming our organisation to be faster, simpler and more connected. Because we want to be even better equipped to develop digital solutions and experiences that help more people live Healthier, Longer, Better Lives.

To get there, we need people with tech/digital/analytics expertise and passion to help develop positive, sustainable change through digitally enhanced experiences that will impact the lives of millions of people and create a healthier future for everyone.

If you believe in developing a better tomorrow, read on. 

About the Role

Reporting line: Manager, Data Science
Location: Hochiminh City
Function: Customer & Information Technology | Department: Data Science
Role: Individual Contributor

The Opportunity:
We are looking for AI Engineering, Specialist who is responsible for
- Designing, building, and scaling mobile-first, LLM-powered agentic systems that move beyond conversational interfaces to perform secure, real-world actions on behalf of users.
- Driving the transformation of AI assistants into high performance mobile experiences while orchestrating multistep agentic workflows integrated with core business APIs.
- Emphasizing low latency performance, accuracy, security, and regulatory compliance, ensuring AI outputs are reliable, explainable, and safe for production use.
- By combining engineering excellence with advanced AI orchestration, enabling intelligent automation, operational efficiency, and measurable business value.

Roles and Responsibilities:

A. AI Solution Delivery (Mobile & Agentic Systems)

- Develop, and deploy LLM-powered AI assistants with a mobile-first design philosophy.

- Select and apply appropriate AI patterns (RAG, tool use, function calling, agent orchestration, streaming inference).

- Ensure AI solutions are secure, reliable, scalable, cost-efficient, and production-ready.

B. Agentic Workflow Orchestration & Automation

- Design and implement agentic AI systems that execute actions (e.g., policy checks, claim summaries, workflow triggers).

- Orchestrate multi-step reasoning and action flows using frameworks such as LangChain, LangGraph, or AI SDKs.

- Define decision logic, guardrails, escalation paths, and human-in-the-loop mechanisms.

- Integrate AI agents with internal systems and APIs using robust and secure patterns.

C. Full-Stack AI Engineering

- Design, build, and maintain AI applications using modern frontend (web/mobile) and backend technologies.

- Develop scalable APIs and services to support real-time inference, agent execution, and asynchronous workflows.

- Build and contribute reusable components, shared libraries, and internal platforms to accelerate team delivery.

D. Latency Optimization & Performance Engineering

- Optimize Time to First Token (TTFT), streaming output behavior, and overall perceived latency.

- Design and implement efficient:

o Data access patterns

o Caching and batching strategies

o Streaming and async pipelines

· Instrument, monitor, and continuously improve production performance metrics.

· Partner with platform and infra teams to diagnose and resolve performance bottlenecks.

E. Accuracy, Evaluation & Quality Assurance

- Build and maintain automated evaluation pipelines to measure accuracy, hallucination rates, and response quality.

- Define acceptance thresholds and quality gates for production deployments.

- Implement test cases for policy-critical and regulation-sensitive scenarios.|

- Apply continuous feedback loops to improve agent reliability and correctness.

F. Security, Privacy & Compliance

- Implement security controls to protect PII and sensitive customer data.

- Mitigate AI-specific risks such as prompt injection, data leakage, and unauthorized actions.

- Ensure AI outputs and workflows comply with insurance regulations and internal governance policies.

- Collaborate with security, legal, and risk teams to operationalize AI guardrails.

G. Continuous Innovation

- Stay current with emerging trends in mobile AI, agentic systems, and applied AI

Requirements:

- Bachelor’s degree in any field with an emphasis on quantitative analysis and applied quantitative analysis such as Mathematics, Economics, Engineering, Statistics, Computer Science etc.

- At least 4 years of hands-on experience in artificial intelligence, machine learning, software engineering, or a related technical field (e.g., full-stack or mobile engineering with AI systems). Experience should include designing, building, and deploying production-grade LLM/AI solutions, developing agentic workflows, and operationalizing AI systems with strong engineering practices (e.g., orchestration frameworks, evaluation pipelines, security guardrails, and performance optimization).

- Prior experience deploying and maintaining AI solutions in production environments, including monitoring, iteration, and optimization, is required.

- Industry preference: Banking and Financial Service Industry (BFSI), Pharma and health care, entertainment, ecommerce, retail and wholesale and manufacturing.

- Personal Projects (good to have): Experience building side projects in ML/AI/GenAI, including hands-on experimentation with emerging AI tools, frameworks, and platforms. This may include developing LLM-powered applications, agentic workflows, mobile or AI prototypes, evaluation frameworks, or lightweight production pipelines that demonstrate practical application, learning agility, and end-to-end ownership.

- Tech-stack: Next.Js, Typescript, Python, Postgres, DataBricks, Redis.

- AI & GenAI: NLP and multimodal systems; time-series analysis; Generative AI including RAG architectures, prompt engineering, tool use, and agentic patterns.

- Data & Storage: Strong proficiency in Python and SQL; experience with analytical processing (e.g., Spark); feature engineering; familiarity with vector databases (Postgres) and embedding pipelines.

- AI Engineering & Productionization: Production AI workflows including model and artifact versioning and experiment tracking.

- Cloud & Infrastructure: Hands-on experience with Cloud (Prefer: Azure stacks); performance optimization for inference and training workloads.

- Observability & Evaluation: End-to-end observability using logging, metrics, and tracing; model and agent monitoring; automated evaluation.

- Responsible AI & Security: Practical application of Responsible AI principles including privacy-by-design (PII handling, masking, de-identification), explainability, robustness, and alignment with regulatory and policy requirements.

- Coding: Apply strong engineering practices:

o Clean architecture and modular design

o Automated testing (unit, integration, E2E)

o Documentation and maintainability standards

- Preferred Traits: The company seeks a proactive, detail-oriented individual who is curious and passionate about data. They should be a team player with strong interpersonal skills.

- Analytical Thinking: Strong problem-solving skills and the ability to think critically about data.

- Attention to Detail: High level of accuracy and attention to detail in data analysis and reporting.

- Collaboration: Ability to work well with cross-functional teams and stakeholders.

- Adaptability: Flexibility to adapt to changing business needs and priorities.

- Thrill: Career Advancement Potential: This position offers the potential to move into senior data Scientist roles, AI Engineering, ML System Architecting, MLOPs, or management roles within the data department, depending on performance and career aspirations.

Build a career with us as we help our customers and the community live Healthier, Longer, Better Lives.

You must provide all requested information, including Personal Data, to be considered for this career opportunity. Failure to provide such information may influence the processing and outcome of your application. You are responsible for ensuring that the information you submit is accurate and up-to-date.