See you @ AI Team
As a company, we are taking the first steps in creating an AI team. Currently there are 3 people in the team + only you are missing! Our recruitment need is in the fine-tuning stage, so this is not a recruitment for a project.
If you want to learn more about how we can grow the business together, watch the new video on LinkedIn -check it out!
Let's keep doing it together!
Your Responsibilities:
- Design and implement AI/ML solutions: Collaborate with the team to design and develop AI/ML models that integrate with existing systems and new applications.
- Work with LLM and GenAI frameworks: Build, test, and deploy applications using frameworks like LangChain and LamaIndex to develop LLM-driven solutions.
- Experiment with open-source models: Test, fine-tune, and deploy open-source AI/ML models, contributing to projects leveraging community-driven innovations.
- Implement RAG (Retrieval-Augmented Generation) systems: Integrate RAG techniques to enhance AI models using relevant, real-time data retrieval strategies.
- Deploy production-grade AI solutions: Integrate models into production environments, ensuring scalability, robustness, and continuous monitoring.
- Data pipeline and database management: Work with relational and vector databases, implement data preprocessing, and manage data pipelines to prepare large datasets for modeling.
- Collaborate across teams: Act as a bridge between development, operations, and product teams to ensure smooth AI/ML model integration.
- Knowledge sharing and mentorship: Help build a collaborative culture by sharing knowledge, guiding junior developers, and contributing to team learning sessions.
- Continuous learning: Stay up-to-date with advancements in AI/ML/GenAI and propose new tools and technologies that can benefit our development efforts.
Our Requirements:
- Proficient in Python: Strong command of Python, with experience in AI/ML libraries such as TensorFlow, PyTorch, and Scikit-learn.
- LLM Application Frameworks: Experience working with frameworks for building LLM applications, including LangChain and LamaIndex.
- Open-source model experience: Practical experience in testing, fine-tuning, and deploying open-source models to production environments.
- RAG Implementation: Familiarity with integrating Retrieval-Augmented Generation systems in AI/ML projects.
- Database experience: Competence in working with relational databases and vector databases for efficient data handling and retrieval.
- Model deployment knowledge: Familiarity with deploying models in production environments, leveraging containerization technologies like Docker and orchestration tools like Kubernetes.
- Basic ML knowledge: Solid understanding of machine learning algorithms, including supervised and unsupervised learning techniques.
- Team-oriented and eager to learn: Comfortable working in a team setting, open to sharing knowledge and learning rapidly in a fast-paced AI environment.
- Data pipeline expertise: Experience with data preprocessing and managing data pipelines for AI/ML applications.
Nice-to-Have Skills:
- Experience with large-scale LLM deployment: Familiarity with deploying large language models and using GenAI in real-world applications.
- Understanding of MLOps: Experience with model lifecycle management, monitoring, and MLOps practices.
- Familiarity with additional frameworks: Experience with tools such as Numpy, Pandas, and emerging MLOps tools.
- Background in NLP or computer vision: Experience working with NLP or computer vision models would be an advantage.
- Open to new technologies: Willingness to explore and experiment with cutting-edge AI/ML technologies and tools.
Our recruitment process is only 3 steps long
- Video interview with our Recruitment Team (abt 45 minutes)
- Video interview with our AI Team (abt 90 minutes)
- If any other step of the recruitment process is necessary, we will inform you.
- And finally, the decision, after which we can say “welcome on board.” 🤝
