Senior AI Engineer

Role Overview
This is a production system builder role.
This is not a research- only role.
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.
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.
Key Responsibilities
Backend & System Architecture (Core Responsibility)

Design and build scalable backend systems (REST APIs, microservices).
Optimize performance for real- time or near- real- time processing.
Design clean data models for production scheduling, forecasting, and factory analytics.
Develop data ingestion pipelines from ERP, MRP, IoT devices, cameras, and Excel- based operational data.

Computer Vision (Factory Applications)

Develop and deploy Computer Vision models for factory use cases such as:
Safety monitoring
Defect detection
Production line monitoring
Quality inspection
Object detection & counting
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.
Implement real- time inference pipelines (camera → edge model → backend → dashboard).

Forecasting & Advanced ML

Build anomaly detection systems (inventory risk, constraint prediction).
Translate business decision logic into ML- driven decision- support systems.
Implement time- series models (ARIMA, Prophet, LSTM, Transformer- based models).
Develop forecasting models (demand forecasting, material planning, capacity planning).

LLM & Cloud AI Integration

Design RAG pipelines connecting LLMs with internal data sources.
Build enterprise AI copilots using cloud LLM services (Azure OpenAI or equivalent).
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

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.

Cross- Functional Technical Ownership

Participate in UAT and production troubleshooting.
Take ownership from design → development → deployment → stabilization.
Collaborate with BA to refine and translate business requirements into technical architecture.
Support QA in defining test scenarios for AI systems.
Handle ad- hoc system issues in factory or supply chain environments.

. Required Qualifications

Solid understanding of system design and distributed architecture.
Experience deploying models to production (not only training).
Strong SQL and database design knowledge.
5+ years of experience in AI/ML or backend engineering.
Experience with Docker and CI/CD pipelines.
Hands- on experience in Computer Vision model development.
Experience with time- series forecasting models.
Strong knowledge of ML frameworks (PyTorch, TensorFlow, Scikit- learn).
Strong Python proficiency (FastAPI, Flask preferred).

Preferred Qualifications

Experience with edge computing or on- prem inference deployment.
Familiarity with GPU optimization and model performance tuning.
Knowledge of Azure cloud services (AI, storage, compute).
Experience integrating with Microsoft ecosystem (Teams, Outlook APIs).
Experience deploying AI systems in manufacturing or factory environments.
Experience building hybrid AI architecture (local + cloud).

Soft Skills

System thinking — able to see end- to- end impact.
Strong ownership mindset and execution capability.
Strong troubleshooting capability in production environments.
Clear communication with technical and non- technical stakeholders.
Able to operate in ambiguous and evolving environments.

What Success Looks Like
Within 12 months, you will have:

Deployed real- time Computer Vision systems in factory environments.
Implemented hybrid AI architecture (on- prem + cloud LLM).
Established CI/CD pipelines for AI deployment.
Built forecasting engines supporting production and supply chain decisions.
Reduced manual operational processes through AI automation.

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Cập nhật gần nhất lúc: 2026-04-16 13:55:05