Job Location: The Great Bharat
Job Detail:
The role
As a Machine Learning engineer at 3lpus1.ai , you will be working on a number of exciting problems across a wide range of use cases, like generative design, image creation, and prompt engineering – things aimed at improving the customer experience and the product overall. As a critical member of our engineering team, you will contribute to all aspects of the software life cycle: design, implementation, testing, experimentation, and fixing any bugs you create. We’re looking for someone who is willing to dive deep, experiment and iterate rapidly, and get things done.
Responsibilities
- Develop, fine-tune, and deploy ML models for B2B customer communication and collaboration use cases.
- Collaborate with cross-functional teams to define requirements, design models, and deploy them in production.
- Optimize model performance and accuracy through experimentation, iteration, and testing.
- Build and maintain ML infrastructure and tools to support model development and deployment.
- Stay up-to-date with the latest research and best practices in ML, and share knowledge with the team
Qualifications ✨
- 2-4 years of industry experience in machine learning engineering, with a focus on natural language processing (NLP) and text classification models.
- Experience with ML Ops, including deploying and managing ML models in production environments.
- Proficiency in Python and deep learning frameworks such as PyTorch or TensorFlow.
- Experience with Embeddings and building on top of LLMs.
- Strong problem-solving and analytical skills, with the ability to develop creative solutions to complex problems.
- Strong communication skills, with the ability to collaborate effectively with cross-functional teams.
- Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, or a related field.
Process
- Submit application through LinkedIn post
- Follow the 3plus.ai LinkedIn page for product updates -https://www.linkedin.com/company/3plus1
- Premilinary screening test will be shared with short listed candidates
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