Principal AI Engineer for Enterprise Production & MLOps

Evonik Industries AG; Selangor; On-site; MYR 180,000 - 300,000; Full time

Job summary

Evonik Industries AG is seeking an experienced AI engineer with a decade of expertise in AI, ML, data science, and software engineering. The role requires hands-on work with ML/DL, generative AI, LLMs, RAG, and responsible AI within enterprise AI platforms, data architecture, and cloud-native tools.

Key strengths include Python, SQL, APIs, microservices, and CI/CD, plus strong knowledge of Azure, Docker, Kubernetes, Git, and modern engineering workflows.

Qualifications

• 10 years of experience across AI engineering, ML, software or data science.
• Hands-on ML/DL, generative AI, LLMs, RAG, and responsible AI.
• Strong AI systems and data architecture knowledge.
• Advanced Python and SQL, APIs, microservices, and CI/CD.
• Experience with ML Ops, lifecycle management, monitoring and governance.
• Knowledge of manufacturing or chemical processes and related AI platforms.

Skills

AI engineering

Machine learning

Software engineering

Data science

Deep learning

Generative AI

LLMs

RAG

Model evaluation

Optimization

Responsible AI

Industrial AI

Business process AI

AI systems architecture

Enterprise data architecture

Azure cloud

API-first integration

Secure platform design

AI operationalization

Python

SQL

APIs

Docker

Kubernetes

CI/CD

Azure DevOps

Azure ML

Azure Databricks

Git

MLOps

LLMOps

Lifecycle management

Monitoring

Governance

Deployment pipelines

Performance management

Cloud-native platform engineering

Manufacturing AI

Process modelling

Operations research

SAP BTP

Power Platform

LeanIX

Databricks

Microsoft 365

Tools

Docker

Kubernetes

Git

Job description

Evonik Industries AG is seeking an experienced AI engineer with a decade of expertise in AI, ML, data science, and software engineering. The role requires hands-on work with ML/DL, generative AI, LLMs, RAG, and responsible AI within enterprise AI platforms, data architecture, and cloud-native tools.

Key strengths include Python, SQL, APIs, microservices, and CI/CD, plus strong knowledge of Azure, Docker, Kubernetes, Git, and modern engineering workflows.