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    Machine Learning Operations Engineer - noida, India - QNX Software Systems

    QNX Software Systems
    QNX Software Systems noida, India

    3 weeks ago

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    Description

    Worker Sub-Type:

    Regular

    Job Description:

    BlackBerry – the iconic brand you know and trust – is now a market leading cybersecurity software and services company.

    Creating the gold standard for unified endpoint security (UES) and unified endpoint management (UEM), BlackBerry Spark offers the broadest set of security capabilities, management tools and visibility covering people, devices, networks, apps, and automation. BlackBerry Spark uses artificial intelligence (AI), machine learning and automation to provide improved cyber threat prevention and remediation, while offering transparency across all endpoints for better management and control.

    Among the first to market with full seventh generation AI for cybersecurity, BlackBerry Cylance helps users understand risks and make intelligent decisions to mitigate them before they happen. Today BlackBerry secures 96% of the threat landscape and we were delighted to be named a 2023 Customers' Choice for Gartner Peer Insights for EPP and UEM. Named by Cybersecurity Ventures as 1 of the top 150 companies to watch, we securely connect more than 500 million mobile, desktop and IoT endpoint devices for G7 governments, 9/10 global financial institutions as well as the largest global aerospace, defense, healthcare, automotive and media companies. Chances are, we are more a part of your life today than we ever were as a handset company.

    Come join us as we deliver 'Intelligent Security. Everywhere.'

    Are you the person we are looking for?

    We are looking for a passionate Machine Learning Operations Engineer (MLOps Engineer). You will be responsible for developing and implementing machine learning operations processes and tooling to enable efficient and reliable model development, deployment, monitoring, and retraining using DevOps tools and best practices. You will work closely with data scientists and machine learning engineers to operationalize machine learning models and integrate them into existing systems and workflows.

    In return for your skills, experience and passion, we offer a great salary, bonus & outstanding benefits package. In short, you bring the talent, and we will provide the environment, tools and share our development know-how to accelerate your professional growth & development.

    What you will do

  • Clean, preprocess and analyze large datasets to identify patterns and insights.
  • Develop automated workflows and pipelines for model training, evaluation, deployment and monitoring using Databricks.
  • Implement continuous integration/delivery practices for machine learning including model versioning, testing, and deployment automation.
  • Set up monitoring of deployed models to detect data/concept drift and trigger retraining when needed.
  • Monitor machine learning systems to ensure quality, identify issues, and drive continuous improvement.
  • Build and maintain model registries, metadata tracking, and model governance processes.
  • Perform A/B testing, canary deployments and controlled rollouts of model changes.
  • Collect and analyze model performance, data quality and operational metrics. Generate reports and dashboards.
  • Troubleshoot issues during model deployment and integration. Perform root cause analysis of performance degradations.
  • Implement scalable machine learning serving infrastructure using technologies like TensorFlow Serving, PyTorch Serve, etc.
  • Collaborate with data scientists and ML engineers on best practices for model development, deployment and maintenance.
  • Stay up to date with emerging technologies in MLOps and machine learning.
  • Document model architectures, data processing steps and results.
  • Present findings to stakeholders and provide recommendations.

    • What you need to bring

  • MS in Computer Science, Statistics, or related field with 2+ years of relevant experience.
  • Hands-on experience with machine learning frameworks like TensorFlow, PyTorch, Scikit-Learn etc.
  • Experience deploying and operationalizing machine learning models.
  • Knowledge of machine learning techniques like supervised/unsupervised learning, neural networks, NLP etc.
  • Expertise in DevOps tools like Docker, Kubernetes, Jenkins, Git, AWS/GCP.
  • Strong programming skills in Python. Knowledge of SQL.
  • Understanding of machine learning concepts, algorithms and model development lifecycle.
  • Excellent communication, troubleshooting and problem-solving skills.
  • Passion for machine learning, automation and continuous improvement.
  • Passion for and ability to work independently as well as part of a team.

    • Above & Beyond

  • It would be great if you have knowledge of MlFlow, GitLab, terragrunt/terraform, Databricks and AWS knowledge.

    • #LI-PP1

    Scheduled Weekly Hours:

    40
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