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AI/ML Engineer

  • Posted:
  • Proposals: 22
  • Remote
  • #4484284
  • Awarded
Abdul Basit A.Anshul ..Safeer H.Mohsin A.Mudasir M. + 17 others have already sent a proposal.
  • 3

Description

Experience Level: Entry
Seeking an experienced AI/ML Engineer to advance an AI meeting assistant application. Responsibilities include designing and implementing robust speech-to-text, speaker diarization, and natural language understanding pipelines; fine-tuning transformer models for summarization, action-item extraction, and intent classification; optimizing inference for real-time performance on cloud or edge; ensuring data privacy and scalable deployment. Ideal candidate has proven experience with deep learning frameworks, NLP, ASR, model optimization, and production-ready ML systems.

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Clarification Board Ask a Question

  • Satwinder S.

    Hi, thank you for sharing the brief. To ensure I provide a targeted proposal, could you clarify a few points:

    1. Speech-to-Text & ASR: Do you have a preferred ASR engine or model (e.g., Whisper, Azure, Google, custom trained), or should the solution include evaluating and selecting the best option?
    2. Speaker Diarization: How many speakers are typically in a meeting, and do you need real-time diarization or is post-meeting processing acceptable?
    3. NLP Pipelines: For summarization and action-item extraction, do you prefer extractive, abstractive, or hybrid summarization?
    4. Intent Classification: Are the intents pre-defined, or will the model need to learn dynamic/custom intents over time?
    5. Deployment: Should the system run fully in the cloud, edge devices, or a hybrid setup? Any restrictions on hardware or latency?
    6. Data Privacy & Compliance: Are there specific compliance requirements (GDPR, HIPAA) to follow for storing or processing meeting data?
    7. Integration & APIs: Will this AI assistant integrate with existing platforms (Zoom, Teams, Google Meet) or is it a standalone solution?
    8. Performance Goals: Do you have target latency or throughput metrics for real-time inference?