Machine Learning Engineer

1 day ago

Gurgaon, Haryana, India Nitro Commerce Full time ₹ 15,00,000 - ₹ 25,00,000 per year

Company Description

Nitro Commerce offers AI tools to unlock the full potential of India's rapidly growing e-commerce market, with a focus on Direct-to-Consumer (D2C) brands. With a decade of experience in crafting revolutionary marketing technology solutions for leading Indian commerce brands, we understand the challenges faced in this ever-evolving landscape. We equip brands with AI-powered solutions to overcome obstacles such as anonymous visitors, complex marketing, and clunky conversions. Join us on our mission to transform the future of Indian e-commerce by building tools, partners, and growth engines for tomorrow's leaders.

What will you work on?/Key Responsibilities:

  • Design, implement, and deploy
    Machine Learning models
    for key AdTech functionalities, including:
  • Click-Through Rate (CTR)
    and
    Conversion Rate (CVR)
    prediction.
  • Real-time Bidding (RTB)
    and budget optimization algorithms.
  • User segmentation
    and
    product recommendation
    specific to quick-commerce buying patterns.
  • Brand safety
    and ad quality filtering.
  • Collaborate with Data Science and Product teams to translate business needs (e.g., maximizing ad spend efficiency, increasing brand visibility) into scalable ML solutions.
  • Manage the
    end-to-end ML lifecycle
    (data processing, feature engineering, model training, deployment, A/B testing, and monitoring) in a high-throughput, low-latency environment.
  • Optimize model performance and maintain data pipelines using cloud-native technologies

What are the perks

  • Freedom to experiment, fail and learn fast, and build cool things
  • A healthy work culture that rewards curiosity and out of box thinking
  • Compensation corresponding to industry standards, experience and technical acumen, with generous ESOPs

Who are we looking for?

  • 1+ year
    of professional experience in Machine Learning
  • Strong proficiency in Python and ML libraries (e.g., TensorFlow, PyTorch, Scikit-learn).
  • Experience with
    large-scale data processing
    using tools like Spark or similar distributed systems.
  • Solid understanding of statistical modeling, model evaluation, and techniques for handling
    imbalanced data
    .
  • Familiarity with containerization (Docker, Kubernetes) and MLOps principles.

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