Welcome to Preplaced! We are looking for a Freelance AI ML Engineer with expertise in LLMops and Python server environments. This role involves building and optimizing the AI backend infrastructure, focusing on the integration of LLM, RAG, and other functionalities within the Python context.
**Preplaced** is a mentorship platform dedicated to career growth and job preparation. With over 500 mentors from leading companies, we offer long-term mentorship programs ranging from 2 to 6 months. Our mentorship loop involves continuous evaluation, strategic planning, execution, and iterative refinement to ensure the holistic development of our mentees. As we embark on exploring the integration of AI into our platform, we are seeking a talented AI Architect and Backend Developer to join our team.
## Responsibilities
### 1. AI Infrastructure Development (LLMops)
- Build and maintain the AI backend infrastructure (preferably working with Python).
- Enhance and adapt the Python server architecture for seamless integration of LLM, RAG, and other AI functionalities.
- Effective Prompt Engineering to get the initial desired output from the LLMs
- Set up and optimize the Vector Database for efficient data storage and retrieval.
### 2. Continuous Improvement
- Work on the continuous improvement and optimization of the LLMops model and related functionalities.
- Implement strategies for fine-tuning and adapting the AI infrastructure to evolving mentorship needs.
### 3. Security and Compliance
- Implement robust security measures specific to the server environment to ensure data protection and compliance with regulations.
- Regularly update systems to address security vulnerabilities and maintain data privacy.
## Qualifications
- Proven expertise in AI infrastructure development within server environments.
- Strong programming skills in Python, with experience in server-side development.
- Familiarity with Vector Databases and mechanisms for RLHF.
- Experience with fine-tuning processes for AI models, especially in a server setting.
- Knowledge of cloud platforms (AWS, Azure, or Google Cloud) and containerization technologies.
- Relevant certifications or equivalent practical experience in AI infrastructure.
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