Welcome to CloudWalk - a fintech company that is committed to revolutionize the payments industry by harnessing the transformative potential of large language models (LLMs). We are seeking a Machine Learning Engineer to join one of our Conversational AI teams, where you will turn advanced LLM research into impactful real-world applications.

Conversational AI at CloudWalk:

Our Conversational AI teams are shaping the future of fintech, integrating LLMs with graph workflows and retrieval-augmented generation (RAG) to address a wide array of challenges. Projects range from customer-facing bots that streamline financial management to internal systems that improve operational efficiency.

We operate at the intersection of research and application, alternating between phases of deep exploration into new technologies and focused delivery of impactful solutions. This dynamic approach ensures our innovations are practical and deliver genuine benefits to our customers.

Our team:

Our team is on a mission to empower customers to manage their finances entirely through a conversational interface. From executing transactions to delivering real-time business analytics, we leverage LLMs to create seamless and engaging user experiences.

Collaboration is at the heart of our team’s culture, where data scientists, machine learning engineers, and software engineers come together to tackle challenges, learn from failures, and celebrate successes. This synergy drives our ability to innovate and deliver transformative results.

Your role:

You will engage in a dynamic workflow that alternates between exploration and exploitation, focusing on enhancing knowledge integration, optimizing performance, and ensuring low-latency responses. Whether experimenting with research-driven ideas or implementing practical solutions like knowledge base updates and system optimizations, your work will directly shape customer experiences. Your contributions might include:

- Refining and enhancing existing RAG pipelines to improve accuracy, scalability, and low-latency performance.

- Exploring and prototyping innovative methods for knowledge integration and retrieval strategies to enhance system performance and user experience.

- Designing and implementing workflows to manage conversation flows, particularly for high-risk scenarios requiring user confirmation.

- Updating and maintaining knowledge bases to ensure accuracy, consistency, and fast retrieval times, while developing semi-automated solutions to streamline the process.

- Monitoring and optimizing system performance, identifying and addressing latency bottlenecks to ensure reliability and scalability.

You will work collaboratively with data scientists, software engineers, and other stakeholders to align priorities and contribute where your expertise is most impactful.

What we expect:

We recognize that the field of LLM applications is relatively young and rapidly evolving. While we seek candidates with a strong foundation in machine learning and software engineering, we do not expect extensive expertise in every aspect of this emerging domain. Some hands-on experience with LLMs and related technologies is sufficient. What matters most is your practical experience in machine learning, willingness to learn, and ability to adapt in a dynamic and collaborative environment.

Required skills:

Machine learning: Strong foundation in machine learning principles, with hands-on experience in natural language processing (NLP) or large language models (LLMs).
Data science: Proficiency in designing experiments, analyzing results, and iterating based on data-driven insights.
Collaboration: Ability to work effectively with cross-functional teams to integrate ML solutions into production systems.
Python proficiency: Advanced coding skills in Python, including experience with ML pipelines and debugging in production environments.
Retrieval augmented generation: Familiarity with the basics of RAG and a strong willingness to learn advanced systems.
Evaluation and monitoring: Understanding frameworks for evaluating model performance and ensuring reliability.
Elixir: Familiarity with Elixir or willingness to learn, given its importance in our infrastructure and tooling.

Nice-to-have skills:

Conversational AI knowledge: Familiarity with designing conversational systems, including dialogue management, intent recognition, and fallback scenarios.
MLOps experience: Experience with tools for deploying and managing machine learning workflows, such as MLflow or similar frameworks.
Cloud platforms: Familiarity with deploying and scaling applications on platforms like Google Cloud, AWS, or Azure.
Data engineering: Knowledge of data workflows, including preprocessing structured and unstructured data.
Knowledge graphs: Experience working with graph databases or designing workflows to model complex relationships.
Performance optimization: Exposure to optimizing system efficiency, such as reducing latency or scaling for high throughput.

Our recruitment process:

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.