Data Wow

End-to-end data processing and AI solutions

AI Engineer – Mid/Senior

AI Application EngineeringUpdated on Nov 14

Full-timeBangkok, ThailandNo salary listedHybrid

About the Job

Requirements

At least 3 years of software engineering experience, including at least 1.5 years focused on AI/ML systems in production environments.
Hands-on experience building with large language models, including prompt engineering, retrieval-augmented generation pipelines, fine-tuning, and evaluation.
Experience designing and deploying agentic AI workflows involving multi-step reasoning, tool use, memory, and planning.
Understanding of inference trade-offs, including latency, throughput, concurrency, batching, model selection, token usage, and infrastructure cost.
Strong Python programming skills and comfort with APIs, cloud services such as AWS, GCP, or Azure, and enterprise databases.
Proven ability to work directly with enterprise customers or technical stakeholders and translate complexity into clarity.
End-to-end ownership mindset, with responsibility extending beyond code delivery to customer success.

Responsibilities

Embed with enterprise customers to understand their workflows, data environments, and operational constraints, and build AI solutions suited to those environments.
Own implementations end-to-end, from scoping and solution design through integration, testing, deployment, and handoff.
Diagnose and resolve technical blockers, including messy data, broken integrations, edge cases, and legacy-system issues.
Design, build, and orchestrate multi-step AI agents that automate complex workflows across enterprise systems.
Use frameworks such as LangGraph, LangChain, or similar to architect reliable, production-ready agentic pipelines.
Determine what should be automated versus what requires human involvement, and design workflows accordingly.
Continuously tune agent behavior based on observed real-world usage patterns.
Build custom integrations, connectors, and data pipelines connecting enterprise technology stacks with AI infrastructure.
Work across APIs, vector databases, large language model APIs, cloud infrastructure, and front-end surfaces to deliver complete systems.
Write clean, maintainable code that others can build on.
Identify patterns across customer engagements that indicate product opportunities and communicate them to engineering and product teams.
Contribute to internal tooling and reusable delivery assets, including deployment templates, agent blueprints, and evaluation frameworks.

Desired Qualifications

Experience with MLOps/LLMOps, including CI/CD, model and prompt version management, automated evaluation, observability, tracing, deployment, and rollback.
Optionally, take ownership of building product features or internal tools informed by field insights.

AI Engineer (Mid/Senior)

Data Wow Co., Ltd. · Bangkok, Thailand (Hybrid) · Full-time · Posted 2026-10-16

Workplace: hybrid

Department: Data Engineer

Description

About the Role

As an AI Engineer on our Enterprise Systems team, you will be embedded directly in the trenches of our most strategic enterprise accounts. You'll take AI from proof of concept to production, navigate the chaos of real customer environments, and make complex agentic systems actually deliver business outcomes.

This is a consulting and hands-on delivery role. You will write production-grade code, architect intelligent agent workflows, debug pipelines at midnight before a go-live, and then walk into a boardroom the next morning to explain what you built and why it matters.

You'll own the full arc — from discovery and design through deployment and adoption — and your success is measured by one thing: does the customer's business actually change because of the AI you delivered?

If you love AI deeply, want to get your hands dirty across the entire delivery lifecycle, and feel equally at home talking to a team and writing LLM orchestration logic, this role was written for you.

What You'll Do

Enterprise AI Delivery

Embed with enterprise customers to understand their workflows, data environments, and operational constraints — and build AI solutions that fit their reality, not a sanitized sandbox
Own implementations end-to-end: from scoping and solution design through integration, testing, deployment, and handoff
Rapidly diagnose technical blockers — messy data, broken integrations, edge cases, legacy system quirks — and solve them yourself without waiting on a queue

Agentic AI Engineering

Design, build, and orchestrate multi-step AI agents that automate complex workflows across enterprise systems
Work with frameworks like LangGraph, LangChain, or similar to architect reliable, production-ready agentic pipelines
Apply sound judgment about what should be automated vs. what requires human-in-the-loop, and design accordingly
Continuously tune agent behavior based on real-world usage patterns you observe in the field

Technical Breadth

Build custom integrations, connectors, and data pipelines to bridge enterprise tech stacks with AI infrastructure
Work across the stack — APIs, vector databases, LLM APIs, cloud infrastructure, and front-end surfaces — to deliver complete, working systems
Write clean, maintainable code that others can build on top of

Product & Feedback Loop

Identify patterns across customer engagements that signal genuine product opportunities — and advocate for them with engineering and product teams with precision
Contribute to building internal tooling and repeatable delivery assets (deployment templates, agent blueprints, evaluation frameworks) that make the next implementation faster
Optionally: take ownership of building product features or internal tools that emerge from your field insights

Requirements

Must-Haves

3+ years of software engineering experience, with at least 1.5 years focused on AI/ML systems in production environments
Hands-on experience building with LLMs (OpenAI, Anthropic, Gemini, or open-source models) — prompt engineering, RAG pipelines, fine-tuning, evaluation
Experience designing and deploying agentic AI workflows — multi-step reasoning, tool use, memory, planning
Understanding of inference trade-offs including latency, throughput, concurrency, batching, model selection, token usage, and infrastructure cost
Strong programming skills in Python; comfortable with APIs, cloud services (AWS/GCP/Azure), and enterprise databases
Proven ability to work directly with enterprise customers or technical stakeholders — you can translate complexity into clarity
End-to-end ownership mindset: you're not done when the code ships; you're done when the customer succeeds
Experience with MLOps/LLMOps; CI/CD, model/prompt/version management, automated evaluation, observability, tracing, deployment, and rollback (Plus)

Benefits

Competitive Salary
Flexible working hour
Hybrid Working
Group health insurance
You pick your equipment (Mac / Windows)
Grab Transportation
Free snacks & drinks (at the office)
Pay 100% for Job-related Training Courses

Apply

Apply at Data Wow Co., Ltd.

About the Company

Data Wow

End-to-end data processing and AI solutions

Data Wow provides end-to-end data processing and AI/ML solutions across data labeling, data management, and deployment, plus web/app development, data pipelines, data warehouses, analytics, and data security. It delivers full-service data labeling tools and data engineering, predictive analytics, and tech/AI consulting to help organizations turn data into actionable insights. By leveraging domain-specific knowledge and a strong Japanese partnership, Data Wow works across industries to tailor solutions for startups to large enterprises, aiming to deliver measurable, data-driven value. The company’s goal is to help clients solve business problems with data-driven systems that produce tangible, trackable results.

Company size: 51-200
Company stage: Not listed
Total funding: Not listed
Headquarters: Bangkok, Thailand
Founded: 2016

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