AI Engineer at GTEMAS

Confirmed open

GTEMAS seeks an AI engineer to deliver production AI applications spanning conventional ML, computer vision and LLM features. The posting covers middle and senior levels.

GTEMAS

Search jobs by company: GTEMAS

Location: Remote
Advertised region: VN / Vietnam
Employment: CONTRACTOR
Work arrangement: remote
Technologies: Python, AWS, PyTorch, TensorFlow

See how to apply

Save job — free account Saved jobs

Create a free application profile — Keep your skills and projects in a reusable profile. Private by default; you choose when to share. Apply for this job directly with the employer.

By continuing, you agree to processing needed to manage your account and profile. Privacy policy

Role overview

You build AI applications that hold up in production - whether that's a classic predictive model, a computer vision pipeline, or an LLM-based feature - because you're not a single-tool specialist. At the Middle level, you build and evaluate ML/GenAI pipelines using an established architecture. At the Senior level, you also fine-tune and productionize models yourself, choose the right approach (classic ML vs. LLM) for the problem, design the model-serving and monitoring architecture, and are the one who diagnoses why a model's output quality degraded in the wild.

Responsibilities

Select, train and evaluate models for classification, regression, recommendation or vision problems; use an LLM only where it suits the task.
Build GenAI and retrieval systems with tools such as Llama, LangChain, Pinecone or Milvus, and fine-tune models in PyTorch or TensorFlow.
Deploy, version and monitor models through MLOps, expose capabilities through APIs, and at senior level own serving architecture and investigate quality regressions.

Qualifications

At least two years of software engineering focused on AI/ML for middle level, or four for senior level; strong Python and PyTorch or TensorFlow skills.
Hands-on work in classic ML, computer vision or NLP/LLM integration and vector databases, plus cloud model deployment such as SageMaker or Vertex AI.
Solid computer-science, mathematics or quantitative foundations and fluent spoken and written English; senior applicants need a record of fine-tuning and productionizing models across different ML problems.

Technology in this role

Python: Python AWS PyTorch .
AWS: Python AWS PyTorch .
PyTorch: Python AWS PyTorch .
TensorFlow: Python AWS PyTorch .
LLM: You build AI applications that hold up in production - whether that's a classic predictive model, a computer vision pipeline, or an LLM-based feature - because you're not a single-tool .
LangChain: Build GenAI and retrieval systems with tools such as Llama, LangChain, Pinecone or Milvus, and fine-tune models in PyTorch or .

Role-specific candidate guide

Preparation notes based on the employer’s stated responsibilities and qualifications.

Understanding the scope. This AI Engineer position at GTEMAS is best assessed through the work described in the employer’s posting, rather than the title alone. The available role overview says: “You build AI applications that hold up in production - whether that's a classic predictive model, a computer vision pipeline, or an LLM-based feature - because you're not a single-tool specialist. At the Middle level,…” Read that statement together with the responsibilities below to understand the likely balance between delivery, coordination and technical decision-making. The role belongs to the AI and data area, but that label is only a guide to the work; the employer’s own requirements remain decisive. Before applying, check the original listing for reporting lines, product or team assignment, employment terms and any details that may have changed since this copy was checked. Where this page says a fact is not specified, treat it as an open question rather than an implied promise.

What the responsibilities signal. The posting specifically names “Select, train and evaluate models for classification, regression, recommendation or vision problems; use an LLM only where it suits the task.”; “Build GenAI and retrieval systems with tools such as Llama, LangChain, Pinecone or Milvus, and fine-tune models in PyTorch or TensorFlow.”; “Deploy, version and monitor models through MLOps, expose capabilities through APIs, and at senior level own serving architecture and investigate quality regressions.”. For each duty, identify a project where you performed comparable work and be ready to describe the context, your own contribution, constraints, decisions, and measurable result. Explain how you moved from the initial need to implementation, review, release or operational follow-through, where those stages apply. If the work involved other teams or users, distinguish your decisions from the group’s contribution. Avoid presenting a technology keyword as proof by itself: connect it to the problem you solved, the scale or complexity involved, and what changed after your work. The source may contain additional duties; review it directly before preparing your application.

How to read the requirements. The listed qualifications include “At least two years of software engineering focused on AI/ML for middle level, or four for senior level; strong Python and PyTorch or TensorFlow skills.”; “Hands-on work in classic ML, computer vision or NLP/LLM integration and vector databases, plus cloud model deployment such as SageMaker or Vertex AI.”; “Solid computer-science, mathematics or quantitative foundations and fluent spoken and written English; senior applicants need a record of fine-tuning and productionizing models…”. Mark each requirement as.

About GTEMAS

GTEMAS (Global Tech Masters) presents itself as a global engineering partner and talent ecosystem. It connects businesses with software professionals to build digital products, scale delivery teams and support longer-term engineering partnerships. The company describes a model that combines technical assessment, structured delivery and professional development for its engineering community.

Its public materials highlight work across software engineering, quality, cloud and security, with an emphasis on measurable delivery, learning and client collaboration. A role at GTEMAS may serve a client engagement, so the specific product, customer context and day-to-day team can differ by assignment. Candidates should ask which project this vacancy supports and how responsibilities are divided between the GTEMAS team and the client.

Employer website