Webkul Jobs 2024 Hiring Machine Learning Engineer- Bachelor’s Degree: Apply Today
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Webkul Started Hiring
| * Role: | Machine Learning-Engineer |
| * Salary: | 4.5LPA(Expected) |
| * Location: | Bangalore |
| * Experience: | Freshers |
| * Education | BE/B-Tech/Bachelor’s Degree |
| * Batch | Any-Batch |
Table of Contents
Webkul Jobs 2024 Hiring Machine Learning Engineer- Bachelor’s Degree: Apply Today
The detailed eligibility Criteria and application process are mentioned below.
Job Description
Machine Learning
We are seeking a highly skilled and motivated Machine Learning Engineer to join our deploying, and optimizing machine learning models that drive our innovative products and services.
| Experience | 00 – 02 Years |
| Openings | 5 |
| CTC | Best in the Industry |
| Interview Process | 1) Coding Round. 2) Technical F2F Interview. 3) HR Round. |
| Job Location | Noida-Sec 63 |
| Education | B.TECH-CS/IT/MCA |
What you will be doing at Webkul?
- Python Proficiency and API Integration:
- Demonstrate strong proficiency in Python programming language.
- Design and implement scalable, efficient, and maintainable code for machine learning applications.
- Integrate machine learning models with APIs to facilitate seamless communication between different software components.
- Machine Learning Model Deployment, Training, and Performance:
- Develop and deploy machine learning models for real-world applications.
- Conduct model training, optimization, and performance evaluation.
- Collaborate with cross-functional teams to ensure the successful integration of machine learning solutions into production systems.
- Large Language Model Understanding and Integration:
- Possess a deep understanding of large language models (LLMs) and their applications.
- Integrate LLMs into existing systems and workflows to enhance natural language processing capabilities.
- Stay abreast of the latest advancements in large language models and contribute insights to the team.
- Langchain and RAG-Based Systems (e.g., LLamaindex):
- Familiarity with Langchain and RAG-based systems, such as LLamaindex, will be a significant advantage.
- Work on the design and implementation of systems that leverage Langchain and RAG-based approaches for enhanced performance and functionality.
- LLM Integration with Vector Databases (e.g., Pinecone):
- Experience in integrating large language models with vector databases, such as Pinecone, for efficient storage and retrieval of information.
- Optimize the integration of LLMs with vector databases to ensure high-performance and low-latency interactions.
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