Senior Machine Learning Engineer

About Company :

Position Overview

Location:

Job Type

Experience

Role Summary

As a Senior Machine Learning Engineer you will be involved in developing and optimizing Generative AI models and solutions that enhance our offerings. This role requires a blend of technical expertise and abstract thinking to build AI-driven solutions for CX industry that are not only innovative but also scalable and impactful.

Responsibilities:

• Develop and deploy

  Generative AI models

  ,

  LLMs

  , and

  Transformer-based architectures

  to build AI-driven customer experience products.
• Design and implement

  machine learning pipelines

  for large-scale data processing, model training, validation, and deployment.
• Work with

  vLLM

  ,

  Triton

  ,

  TensorRT

  , and other high-performance inference frameworks to optimize model serving and reduce latency.
• Build and integrate

  audio and image encoder models

  , multimodal models, and vision/language components.
• Develop and maintain

  speech-to-text (ASR)

  and

  text-to-speech (TTS)

  systems for voice-enabled applications.
• Train, fine-tune, and evaluate ML and GenAI models using best practices in deep learning, data preparation, and model optimization.
• Monitor, analyze, and improve model performance to meet business KPIs and product requirements.
• Research and implement the latest techniques in

  Generative AI, deep learning, multimodal AI

  , and advanced ML architectures.
• Collaborate closely with product, engineering, and data teams to convert business needs into scalable AI solutions.

Qualifications:

• Bachelors degree in

  Computer Science, Artificial Intelligence, Data Science

  , or related field.

• 6+ years of hands-on experience

  building and deploying machine learning or deep learning models (GenAI experience preferred).
• Strong programming skills in

  Python

  and experience with frameworks such as

  PyTorch

  ,

  TensorFlow

  , or JAX.
• Solid understanding of

  Transformers

  ,

  LLMs

  , attention mechanisms, and model training pipelines.
• Experience with

  vLLM

  ,

  Triton

  ,

  TensorRT

  , GPU acceleration, and model optimization techniques.
• Practical experience with

  audio encoders, image encoders, vision transformers

  , and multimodal architectures.
• Experience building or integrating

  ASR (speech-to-text)

  and

  TTS (text-to-speech)

  models.
• Hands-on experience with

  training, fine-tuning

  , and evaluating large deep learning models.
• Knowledge of

  AWS, GCP, or Azure

  for cloud-based model deployment (preferred).
• Strong analytical, problem-solving, and teamwork skills with the ability to work in fast-paced environments.