Machine Learning Engineer II
6 days ago
Position : ML Engineer
Experience : 46 Years
Location : Bangalore
Job Type : Full-time
Job Summary :
We're looking for a skilled and experienced ML Engineer 2 with 46 years of experience in applied machine learning and model deployment. The ideal candidate will have hands-on experience with advanced technologies such as Generative AI, NLP, or Conversational AI.
This role is crucial for building and deploying production-level machine learning solutions that deliver measurable value. You'll be responsible for designing and implementing robust ML pipelines and working with cloud services to deploy scalable models.
Key Responsibilities :
- Applied Machine Learning: Design, develop, and deploy machine learning models and solutions, with a specific focus on Generative AI, NLP, or Conversational AI technologies.
- MLOps & Deployment: Build and maintain end-to-end ML pipelines and workflows. You'll use cloud services (AWS, GCP, or Azure) to deploy models to production environments.
- Model Development & Engineering: Apply strong proficiency in Python and commonly used ML frameworks (PyTorch, TensorFlow, Hugging Face) to develop and refine models. You'll be responsible for feature engineering and ensuring the performance of the models.
- Collaboration & Communication: Work collaboratively with cross-functional teams to translate business problems into machine learning solutions. You'll be expected to communicate complex technical concepts clearly to both technical and non-technical stakeholders.
- Performance & Optimization: Contribute to production-level ML solutions, focusing on performance optimization and ensuring the delivered solutions provide measurable business value.
Required Skills & Qualifications :
years of hands-on experience in applied machine learning and model deployment.
- Hands-on experience with Generative AI, NLP, or Conversational AI technologies.
- Proficiency in Python and commonly used ML frameworks (PyTorch, TensorFlow, Hugging Face).
- Experience with building and deploying models using cloud services (AWS, GCP, or Azure).
- Strong understanding of ML pipelines, feature engineering, and basic MLOps practices.
- A Master's degree in Computer Science, Machine Learning, or a related discipline.
Preferred Skills :
- Exposure to the fintech or financial services domain.
- Familiarity with recommendation engines or personalization techniques.
- Contributions to open-source ML projects or the community.
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