MLOps Engineer

Job updated about 6 hours ago

Job Description

As an MLOps Engineer, you will play a key role in designing, implementing, and maintaining robust machine learning platforms and data pipelines, ensuring smooth deployment, scaling, and monitoring of models in production. You will drive automation to accelerate the development, evaluation, and integration of machine learning models, improving collaboration and overall efficiency. In addition to optimizing production environments, you will act as a bridge between machine learning developers and software engineers, ensuring seamless integration of ML systems into applications. You will also share best practices for MLOps and have the opportunity to work on high-impact projects that reach millions of users, as well as help bring innovative new applications to market.

Requirements

  • Strong programming skills in Python.
  • Proficiency with containerization tools (Docker, Kubernetes) and cloud platforms (GCP, AWS, Azure; expertise in at least one).
  • Experience working with backend servers and APIs (e.g., FastAPI, Django, or similar frameworks).
  • Experience with machine learning frameworks (PyTorch, TensorFlow).
  • Experience with MLOps tools (Kubeflow, MLflow, TFX).
  • Experience with monitoring tools (Prometheus, Grafana) and logging frameworks.
  • Knowledge of data engineering concepts (ETL pipelines, data lakes, data warehouses).



Responsibilities:

  • Design, implement and maintain machine learning platform and data pipelines, ensuring seamless deployment, scaling, and monitoring of models in production environments.
  • Set up monitoring systems for deployed models and tracking key metrics.
  • Apply and share software engineering best practices within the context of machine learning.
  • Collaborate with ML developers to ensure model performance is maintained in production and work with software engineers to integrate ML systems into the broader application stack.
  • Accelerate machine learning development, evaluation, and integration speed through automation of workflows, tools, and processes to enhance collaboration and efficiency.

    Interview process

    After you submit your application, the next steps are one or two video interview calls (with short written questions), followed by an onsite interview. Our interview process includes interesting exercises with our team members (e.g. pair programming, design brainstorming). Through this process, we hope you can get to know what it's like to work with us, and get to know our team and culture!

    2
    2 years of experience required
    1,700,000 ~ 3,000,000 TWD / year
    Optional Remote Work
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    About us

    嗨!我們是來自矽谷的新創團隊 PicCollage Company,我們團隊鼓勵自由創造,擁抱差異與多元文化,打造『開放、學習、分享』的工作環境。產品開發近期以『Creative AI』為主軸,打造使用者能輕鬆發揮創意的各項影像編輯產品。

    『Creative AI』 是創意 AI 的展現和延續

    透過 Generative AI 和 ML 技術的研究,開發出具創意的 AI 並導入產品功能。Creative AI 不僅展現在我們挑選的技術領域,還有主打的產品開發類別,讓使用者在操作 app 的過程中展現更多創意!

    主要產品『PicCollage 拼貼趣』是一個追求自由創造及分享的影像拼貼 app,主要使用者來自美國、英國及日本等國家。目前全球下載量已突破 2.6 億,每個月 1500 萬穩定用戶量,並穩定增長中。

    另外,新產品團隊 Explore 持續開發新 app 產品,像是挑選片段就能產出的對拍影片 (OnBeat)、或是一張頭像就能生成你的專屬迷因 (MemeMe)、同時線上多組討論視訊軟體 (MixerChat) 等。再戰創意領域 app 的下一個產品里程碑!


    我們相信:

    • Always be Learning - 除了發放學習補助金之外,我們也會特別安排時間讓團隊學習不同事物,可以正面的回饋在個人發展及團隊目標!
      • Workshop Day ➤ https://youtu.be/6N6L7KbajII
      • 定期舉辦的 PicCollage 黑客松 : InnoFest ➤ https://youtu.be/kbMPoQ6ifmc
    • Live Well, Work Well - 提供彈性的休假制度及工作模式,讓成員可以更好的安排生活,並在工作上有最傑出的表現!
      • Life at PicCollage ➤ https://www.youtube.com/@piccollageteam4227/featured
    • Win as Team - 重視每位成員表達意見及想法的機會,不論來自哪個部門,都可以為團隊目標盡一份心力!

    期待遇見更多 #builders #dreamers #thinkers 來激盪創意,加入 PicCollage Company 和我們一起做出好玩、富有創意的軟體產品到全世界🌎


    我們提供部分遠端工作:彈性工作地點 (需配合台灣時區參與會議)

    欲更認識我們,歡迎參考以下連結:

    • Our Office ➤ https://www.youtube.com/watch?v=R7jqZdIugRs
    • Our Products ➤ https://picc.co/products
    • Tech Blog ➤ https://tech.pic-collage.com/
    • 成員分享:團隊文化 ➤ https://bit.ly/3zyQrAl

    想更認識 PicCollage 團隊和職缺嗎? 來參加每個月舉辦的 Online Info Session 線上招募同樂會,你能更了解招募中的職缺、並與成員互動 QA唷。現在就報名登記:https://forms.gle/bkHk4QUPC4ACCnMcA



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