- Experience
- 8+ yrs
- Salary
- —
- Openings
- 1
- Posted
- 4 days ago
- Work mode
- In office
- Resume
- Required to apply
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Job description
About ASUS Intelligent Cloud Service (AICS)
AICS focuses on creating Healthcare AI innovations that enhance clinical workflows through advanced large language models (LLMs), agentic AI, and cutting-edge AI engineering practices.
Job Overview
We seek an Engineering Manager to lead and expand a skilled team of Machine Learning Engineers dedicated to developing production-level LLM applications, AI infrastructure, and clinical AI solutions.
Key Responsibilities
- Oversee and nurture a high-performing ML engineering team to achieve technical excellence.
- Guide the design, development, and deployment process of scalable production-ready LLM applications and AI systems.
- Develop and maintain robust AI engineering workflows, including model evaluation, experimentation, deployment, and continuous enhancement.
- Drive the creation of agentic AI functionalities such as multi-agent workflows, tool integrations, memory management, and orchestration mechanisms.
- Define and enforce best engineering practices covering AI software development quality, testing protocols, and MLOps methodologies.
- Collaborate closely with AI Research, Product Management, and Software Engineering to convert research initiatives into production-grade products.
- Keep up-to-date with emerging AI technologies and incorporate advancements that boost product quality and engineering efficiency.
Required Qualifications
- Minimum of 8 years experience in software engineering, machine learning, or AI engineering roles.
- At least 3 years of leadership experience managing technical engineering teams.
- Demonstrated success in delivering AI products to production environments.
Technical Expertise
- Practical, hands-on experience with large language models and generative AI application development.
- Knowledge of model fine-tuning or adaptation tailored to specific domain challenges.
- Proficiency in evaluating and benchmarking LLMs.
- Familiarity with Retrieval-Augmented Generation (RAG) and knowledge retrieval frameworks.
- Development experience with agentic AI architectures and workflow orchestration.
- Skills in prompt engineering and integrating structured tool calls.
- Experience using PyTorch and the Hugging Face ecosystem.
- Competence in AI deployment processes, inference optimization, and MLOps.
- Hands-on with cloud-native technologies including Docker, Kubernetes, and cloud providers such as Azure or Google Cloud Platform.