MACHINE LEARNING ENGINEER - CONVERSATIONAL AI WITH LLMS

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.