Description
Welcome to CloudWalk - a fintech company that is committed to revolutionize the payments industry by harnessing the power of large language models (LLM). We are seeking a machine learning engineer to join one of our Conversational AI teams, transforming cutting-edge LLM research into real-world applications.
Conversational AI at CloudWalk:
Our Conversational AI teams are dedicated to pushing the boundaries of what is possible in chatbot development, leveraging LLMs, graph workflows, and retrieval-augmented generation (RAG). We are working on a range of projects, from customer-facing bots that enhance user experiences to internal bots that streamline workflows.
Our environment is highly dynamic, allowing us to quickly adapt to the latest advancements in research. We strike a balance between exploring cutting-edge technologies and delivering high-impact solutions that genuinely benefit our customers. Collaboration is at the heart of our teams’ cultures. Together we embrace challenges, learn from failures and celebrate successes.
Your responsibilities:
Your role will evolve as you grow with our dynamic teams. Initially, you will join our customer support chatbot team. You will:
- Design and conduct experiments to validate new features, such as enhanced context understanding or improved ability to switch conversation topics.
- Implement promising experiments in production, ensuring scalability and reliability.
- Collaborate with customer support specialists within our cross-functional team to deeply understand business needs and translate them into technical requirements.
As you grow with the team, your role will include:
- Mentor junior developers by sharing your technical expertise and guiding them through complex challenges to enhance the team’s technical capabilities and drive innovation.
- Lead technical discussions that shape the roadmap of our customer support chatbot.
- Spearhead the adoption of new technologies and methodologies within the team.
What we expect:
We recognize that the field of LLMs and RAG are relatively young and rapidly evolving. While we are seeking candidates with a solid background in machine learning, software engineering and some practical exposure to LLMs and RAG, we value machine learning engineers with a track record of success in NLP projects and a passion to expand their expertise in these quickly growing technologies.
Required skills:
- Machine learning: Experience in designing, implementing, and optimizing machine learning models.
- NLP expertise: In-depth knowledge of natural language processing techniques, including text classification, entity recognition, and sentiment analysis.
- Software engineering: Solid software engineering skills with expertise in Python and relevant frameworks and libraries. Strong understanding of software design principles, code optimization, and modular architecture.
- Cloud computing: Experience with cloud platforms for deploying and scaling machine learning models.
- Problem-solving skills: Ability to analyze complex problems, propose effective solutions, and implement them in a production environment.
- Communication skills: Excellent communication skills to collaborate effectively with cross-functional teams, and mentor junior engineers.
- Language proficiency: Fluency in English and Portuguese.
Nice-to-have skills:
- Knowledge retrieval: Experience in optimizing knowledge retrieval systems, working with knowledge bases, and implementing efficient algorithms for retrieving relevant information.
- Prompt engineering: Familiarity with advanced prompting techniques to improve contextual understanding and the quality of the response, as well as common problems that current generation LLMs face
- Knowledge graphs: Experience working with knowledge graphs and semantic technologies to enhance the representation and retrieval of information.
- Deep Learning applied to NLP: Deep understanding of transformers models and derivatives, including knowledge of its submodules, tokenization and generative algorithms. Knowledge about training and fine-tuning algorithms and its use cases.
- Distributed systems: Knowledge of distributed systems and parallel computing to enhance the scalability and performance of the chatbot infrastructure.
- DevOps practices: Understanding DevOps practices, including continuous integration, continuous deployment, and containerization.
Our recruitment process:
The process consists of three stages:
- Online technical assessment: You will be asked to complete an online quiz that evaluates your knowledge in key areas such as machine learning and software engineering principles.
- Technical interview: This in-depth discussion will assess your technical knowledge, problem-solving skills, and experience relevant to the role.
- Cultural interview: We will explore your work style, values, and how you might contribute to and thrive within our team culture.
Our goal is to ensure a good mutual fit and set the foundation for a successful collaboration. We appreciate your time and commitment throughout this process and aim to provide a clear and timely response after each stage.
Diversity and Inclusion: We believe in social inclusion, respect and appreciation of all people. We promote a welcoming work environment, where each CloudWalker can be authentic, regardless of gender, ethnicity, race, religion, sexuality, mobility, disability or education.
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