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  • ML Ops
    DevOps

    ML Ops

    Wrocław
    Type of work
    Full-time
    Experience
    Senior
    Employment Type
    B2B
    Operating mode
    Remote
    Link Group

    Link Group

    Hundreds of IT opportunities are waiting for you—let’s make it happen! Since 2016, our team of tech enthusiasts has been building exceptional IT teams for Fortune 500 companies and startups worldwide. Join impactful projects in BFSI, CPG, Industrial, and Life Sciences & Healthcare industries. Work with cutting-edge technologies like Cloud, Business Intelligence, Data, and SAP. Unlock your potential, grow your skills, and collaborate with top global clients. Ready for your next big career move? Let’s link with us!

    Company profile

    Tech stack

      LLM

      advanced

      AI

      advanced

      Cloud

      regular

      NLU

      regular

    Job description

    Online interview
    Friendly offer

    Key Responsibilities:

    • Model Lifecycle Management: Support the development, training, deployment, and monitoring of machine learning models in production environments.
    • Automation & Optimization: Automate the deployment and provisioning of machine learning models using tools like Docker, Kubernetes, and CI/CD pipelines.
    • Monitoring & Observability: Implement monitoring and observability solutions using tools like the ELK Stack to ensure the health and performance of deployed models.
    • Data Management: Work with data manipulation and transformation processes, including SQL-based solutions, to streamline data for model training and inference.
    • Collaboration: Collaborate with data scientists, software engineers, and cloud engineers to ensure the success of the project.
    • MLOps Best Practices: Utilize MLOps architecture and practices to manage the machine learning pipeline effectively.


    Must-Have Skills:

    • ML Project Experience: Proven work experience in machine learning projects, with hands-on involvement in model lifecycle management.
    • MLOps Tools & Frameworks: Proficiency with tools such as Apache Airflow, sklearn, MLFlow, and TensorFlow.
    • Cloud Environment Experience: Solid experience in cloud platforms, preferably Google Cloud Platform (GCP).
    • Programming Skills: Proficiency in Python for machine learning model development.
    • Monitoring Expertise: Experience with monitoring tools such as the ELK stack.
    • Software Engineering Practices: Familiarity with versioning, testing, documentation, and code review practices.
    • Automation Tools: Expertise in deployment automation tools like Docker, Kubernetes, Openshift, and CI/CD pipelines.


    Nice-to-Have Skills:

    • Distributed Systems & Data Pipelines: Experience with distributed systems and large-scale data processing technologies such as S3, Spark, Kafka, and Flink.
    • Advanced Analytics & Data Science: Affinity with advanced analytics, NLP, and data science workflows.
    • ETL Pipeline Development: Hands-on experience building complex ETL pipelines.
    • System Design: Experience in system design and architecture for scalable machine learning solutions.
    • Linux & Scripting: Proficiency in Bash scripting and Linux systems administration.
    • Static Typed Programming Languages: Experience in programming with Scala or Java.
    • Agile/Scrum Methodology: Familiarity with working in agile/scrum-based project environments.
    • Open-Source Contribution: Contributions to open-source projects are a strong plus.

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