Senior AI Engineer

1. Role Overview
We are looking for a highly hands-on Senior AI Engineer who can design and deploy real-world AI systems — including Computer Vision in factory environments, forecasting engines, real-time processing systems, and LLM-powered enterprise copilots.
This role requires strong backend engineering, ML expertise, DevOps capability, and the ability to deploy both local/on-prem models (factory environment) and cloud-based LLM solutions.
This is not a research-only role.
This is a production system builder role.
2. Key Responsibilities
A. Backend & System Architecture (Core Responsibility)
Design and build scalable backend systems (REST APIs, microservices).
Develop data ingestion pipelines from ERP, MRP, IoT devices, cameras, and Excel-based operational data.
Design clean data models for production scheduling, forecasting, and factory analytics.
Optimize performance for real-time or near-real-time processing.
B. Computer Vision (Factory Applications)
Develop and deploy Computer Vision models for factory use cases such as:
Quality inspection
Defect detection
Object detection & counting
Production line monitoring
Safety monitoring
Implement real-time inference pipelines (camera → edge model → backend → dashboard).
Optimize models for on-prem/edge deployment (low latency, resource constraints).
Work with OpenCV, YOLO, CNN architectures, or equivalent frameworks.
Deploy and monitor local inference services inside factory network environments.
C. Forecasting & Advanced ML
Develop forecasting models (demand forecasting, material planning, capacity planning).
Build anomaly detection systems (inventory risk, constraint prediction).
Implement time-series models (ARIMA, Prophet, LSTM, Transformer-based models).
Translate business decision logic into ML-driven decision-support systems.
D. LLM & Cloud AI Integration
Build enterprise AI copilots using cloud LLM services (Azure OpenAI or equivalent).
Design RAG pipelines connecting LLMs with internal data sources.
Implement secure API-based integration between on-prem systems and cloud AI services.
Architect hybrid AI systems:
Local models for factory real-time inference
Cloud LLM for analytics, reasoning, and automation
E. DevOps, CI/CD & Deployment
Containerize applications using Docker.
Build CI/CD pipelines for AI model deployment.
Manage multi-environment deployment (Dev / UAT / Production).
Implement monitoring, logging, and performance tracking for AI systems.
Ensure system reliability and security in enterprise network environments.
F. Cross-Functional Technical Ownership
Collaborate with BA to refine and translate business requirements into technical architecture.
Support QA in defining test scenarios for AI systems.
Participate in UAT and production troubleshooting.
Handle ad-hoc system issues in factory or supply chain environments.
Take ownership from design → development → deployment → stabilization.. Required Qualifications
5+ years of experience in AI/ML or backend engineering.
Strong Python proficiency (FastAPI, Flask preferred).
Strong knowledge of ML frameworks (PyTorch, TensorFlow, Scikit-learn).
Hands-on experience in Computer Vision model development.
Experience with time-series forecasting models.
Experience deploying models to production (not only training).
Strong SQL and database design knowledge.
Experience with Docker and CI/CD pipelines.
Solid understanding of system design and distributed architecture.
4. Preferred Qualifications
Experience deploying AI systems in manufacturing or factory environments.
Experience with edge computing or on-prem inference deployment.
Familiarity with GPU optimization and model performance tuning.
Experience integrating with Microsoft ecosystem (Teams, Outlook APIs).
Experience building hybrid AI architecture (local + cloud).
Knowledge of Azure cloud services (AI, storage, compute).
5. Soft Skills
Strong ownership mindset and execution capability.
Able to operate in ambiguous and evolving environments.
System thinking — able to see end-to-end impact.
Strong troubleshooting capability in production environments.
Clear communication with technical and non-technical stakeholders.
6. What Success Looks Like
Within 12 months, you will have:
Deployed real-time Computer Vision systems in factory environments.
Built forecasting engines supporting production and supply chain decisions.
Established CI/CD pipelines for AI deployment.
Implemented hybrid AI architecture (on-prem + cloud LLM).
Reduced manual operational processes through AI automation.
Ngành nghề: CNTT - Phần mềm
Kinh nghiệm: 5 Năm
Cấp bậc: Nhân viên
Hình thức: Nhân viên chính thức
Địa điểm: Quảng Ngãi

Yêu cầu công việc

. Required Qualifications
5+ years of experience in AI/ML or backend engineering.
Strong Python proficiency (FastAPI, Flask preferred).
Strong knowledge of ML frameworks (PyTorch, TensorFlow, Scikit-learn).
Hands-on experience in Computer Vision model development.
Experience with time-series forecasting models.
Experience deploying models to production (not only training).
Strong SQL and database design knowledge.
Experience with Docker and CI/CD pipelines.
Solid understanding of system design and distributed architecture.
4. Preferred Qualifications
Experience deploying AI systems in manufacturing or factory environments.
Experience with edge computing or on-prem inference deployment.
Familiarity with GPU optimization and model performance tuning.
Experience integrating with Microsoft ecosystem (Teams, Outlook APIs).
Experience building hybrid AI architecture (local + cloud).
Knowledge of Azure cloud services (AI, storage, compute).
5. Soft Skills
Strong ownership mindset and execution capability.
Able to operate in ambiguous and evolving environments.
System thinking — able to see end-to-end impact.
Strong troubleshooting capability in production environments.
Clear communication with technical and non-technical stakeholders.
6. What Success Looks Like
Within 12 months, you will have:
Deployed real-time Computer Vision systems in factory environments.
Built forecasting engines supporting production and supply chain decisions.
Established CI/CD pipelines for AI deployment.
Implemented hybrid AI architecture (on-prem + cloud LLM).
Reduced manual operational processes through AI automation.