職位描述
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Key responsibilities:
- Partner with business, product, and engineering teams to define problem statements, evaluate feasibility, and design AI/ML-driven solutions that deliver measurable business value.
- Lead and execute end-to-end AI/ML projects — from data exploration and model development to validation, deployment, and monitoring in production.
- Drive solution architecture using techniques in data engineering, programming, machine learning, NLP, and Generative AI.
- Champion the scalability, reproducibility, and sustainability of AI solutions by establishing best practices in model development, CI/CD, and performance tracking.
- Guide junior and associate AI/ML engineers through technical mentoring, code reviews, and solution reviews.
- Identify and evangelize the adoption of emerging tools, technologies, and methodologies across teams.
- Translate technical outputs into actionable insights for business stakeholders through storytelling, data visualizations, and stakeholder engagement.
We are looking for:
- A seasoned AI/ML scientist with 4-6 years of hands-on experience delivering enterprise-grade AI/ML solutions.
- Advanced proficiency in Python, SQL, and experience working with cloud platforms (Azure preferred) and tools such as ADF, Function Apps and Web Apps.
- Strong expertise in applied text analytics, NLP, and Generative AI, with real-world deployment exposure.
- Solid understanding of model evaluation, optimization, bias mitigation, and monitoring in production.
- A problem solver with scientific rigor, strong business acumen, and the ability to bridge the gap between data and decisions.
- Prior experience in leading cross-functional AI initiatives or collaborating with engineering teams to deploy ML pipelines.
- Bachelor's or master’s degree in computer science, Engineering, Statistics, or a related quantitative field. A PhD is a plus.
- Prior understanding in business domain of shipping and logistics is an advantage.
- Partner with business, product, and engineering teams to define problem statements, evaluate feasibility, and design AI/ML-driven solutions that deliver measurable business value.
- Lead and execute end-to-end AI/ML projects — from data exploration and model development to validation, deployment, and monitoring in production.
- Drive solution architecture using techniques in data engineering, programming, machine learning, NLP, and Generative AI.
- Champion the scalability, reproducibility, and sustainability of AI solutions by establishing best practices in model development, CI/CD, and performance tracking.
- Guide junior and associate AI/ML engineers through technical mentoring, code reviews, and solution reviews.
- Identify and evangelize the adoption of emerging tools, technologies, and methodologies across teams.
- Translate technical outputs into actionable insights for business stakeholders through storytelling, data visualizations, and stakeholder engagement.
We are looking for:
- A seasoned AI/ML scientist with 4-6 years of hands-on experience delivering enterprise-grade AI/ML solutions.
- Advanced proficiency in Python, SQL, and experience working with cloud platforms (Azure preferred) and tools such as ADF, Function Apps and Web Apps.
- Strong expertise in applied text analytics, NLP, and Generative AI, with real-world deployment exposure.
- Solid understanding of model evaluation, optimization, bias mitigation, and monitoring in production.
- A problem solver with scientific rigor, strong business acumen, and the ability to bridge the gap between data and decisions.
- Prior experience in leading cross-functional AI initiatives or collaborating with engineering teams to deploy ML pipelines.
- Bachelor's or master’s degree in computer science, Engineering, Statistics, or a related quantitative field. A PhD is a plus.
- Prior understanding in business domain of shipping and logistics is an advantage.
工作地點(diǎn)
地址:重慶渝中區(qū)重慶來福士購(gòu)物中心(接圣街店)
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詳細(xì)位置,可以參考上方地址信息
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職位發(fā)布者
KAPO..HR
馬士基信息處理(成都)有限公司

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交通·運(yùn)輸·物流
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200-499人
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私營(yíng)·民營(yíng)企業(yè)
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天府軟件園(天華二路)附近
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2025-12-20 09:58:31
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