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Machine Learning Engineer
Machine Learning EngineerLoglineAI • faridabad, haryana, in
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Machine Learning Engineer

Machine Learning Engineer

LoglineAI • faridabad, haryana, in
13 days ago
Job description

ML Engineer (Studio Applications)

Location : Remote (India)

Level : Mid / Senior (5–10+ years)

About the role

The ML Engineer (Studio Applications) focuses on deploying and maintaining machine learning models that support LoglineAI’s production workflows. The role is primarily applied and production-focused rather than research-oriented.

The engineer will integrate third-party and open-source models for image and video generation, enhancement, and quality control into the platform. They will fine-tune models where necessary, manage inference services, and work with pipeline engineers and any research staff to ensure models perform reliably in real use cases.

Responsibilities :

  • Integrate third-party and open-source models (image / video generation, super-resolution, artifact detection) into LoglineAI’s platform.
  • Fine-tune or adapt models for specific needs such as style preservation, character identity, motion and camera control, and cleanup.
  • Design and operate inference services, including model serving, autoscaling, A / B testing, and performance monitoring.
  • Collaborate with Pipeline Engineers on model selection, parameter choices, and trade-offs between cost and quality.
  • Work with research-oriented staff to convert prototypes into stable, monitored production services.

Requirements :

  • 5–10+ years of experience in machine learning or computer vision, with strong Python and PyTorch / TensorFlow skills.
  • Proven experience deploying models to production (batch or real-time inference).
  • Experience with diffusion or other generative models, ideally including video or sequence-aware work.
  • Familiarity with Docker / Kubernetes, experiment tracking (e.g., Weights & Biases, MLflow), and ML observability practices.
  • Ability to read and implement methods from research papers at a practical level.
  • Ideal Candidate comes from applied CV / ML product teams or ML platform / MLOps environments, working in Machine Learning Engineer, Computer Vision Engineer, Applied Scientist, or Senior Data Scientist focused on ML-heavy, GPU-driven systems.

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