We are not just another fintech unicorn. We are a pack of dreamers, makers, and tech enthusiasts building the future of payments. With millions of happy customers and a hunger for innovation, we're now expanding our neural network - literally and metaphorically.
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.