AI ENGINEER
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Key Responsibilities
- Build & Deploy AI Applications: Design, test, and deploy end-to-end LLM-powered applications and Agentic workflows to automate operational processes and elevate manufacturing quality.
- Production Deployment: Containerize and deploy AI models and software applications into production environments on Microsoft Azure, ensuring high availability and scalability.
- System Integration: Connect AI tools and agent workflows with core manufacturing and enterprise systems (e.g., ERP, MES, internal databases, or IoT endpoints).
- Drive Digitalization: Collaborate cross-functionally with operations, quality control, and engineering teams to identify pain points and build practical, data-driven automation tools.
- Innovation & R&D: Continuously research and prototype emerging GenAI techniques, multi-agent frameworks, and Industry 4.0 solutions to improve operational efficiency.
Requirements
- Experience Level: Fresh graduates and early-career candidates (up to 2 years of relevant experience) with a strong foundation in software or AI project work.
- Core Technical Skills:
- Proficiency in Python and fundamental AI/ML concepts.
- Practical exposure to Generative AI and LLM frameworks (e.g., LangChain, LlamaIndex,
Open AI APIs, or similar).
- Basic familiarity with cloud services (Microsoft Azure preferred).
- Knowledge of software development basics (REST APIs, Git, basic database design).
- Soft Skills: - Strong communication skills and the ability to explain technical concepts to non-technical stakeholders.
- High self-motivation, ownership mindset, and an eagerness to learn complex systems
end-to-end
Preferred (Nice to Have)
- Prior exposure to multi-agent architectures (e.g., AutoGen, CrewAI, LangGraph).
- Basic understanding of Docker, CI/CD pipelines, or MLOps/LLMOps on Azure.
- Hands-on experience with modern frontend or backend frameworks (e.g., FastApi, Flask, Streamlit, React).
