• Support the development of IDP (Intelligent Document Processing) solutions: Contribute to creating, training, tuning models and fine-tuning for extracting unstructured data from complex documents. • Build the future of agentic AI: Assist in orchestrating workflows using frameworks such as LangGraph, n8n, and others, connecting document extraction to approval and formalization pipelines. • Transform data into products: Apply statistical techniques and data analysis to the structured datasets produced by our IDPs, helping identify patterns and insights that will become new AI products for our clients. • Collaborate with technical, product and commercial teams: Work side by side with Technical Leadership, Data Engineering, Product Managers and the Front-end team, evaluating, supporting and improving the transition of proofs of concept (PoCs) to production solutions. • Ensure quality and monitoring: Help evaluate model outputs (LLMs, RAGs and classical models), test prompts and document processes, ensuring solutions are accurate and scalable. • Learn and innovate: Stay up to date with the state of the art in Generative AI, bringing curiosity, ideas and proactivity to solve bottlenecks in our document pipeline.

Role Description We are looking for a savvy Machine Learning/Data Engineer to join our growing team of data experts. The hire will be primarily responsible for AI/ML projects on AWS, leveraging native services as well as custom-built models to deliver predictive insights to our customers. In addition, this hire will also support migrating to the cloud, optimizing our customers’ databases and data flows, and enriching our operational and functional data flow with AI/ML algorithms. The ideal candidate is confident in data in any form or scale and happy to learn and teach new data tools. The candidate enjoys optimizing data systems and building them from the ground up. The Machine Learning Engineer will support new system designs and migrate existing ones, working closely with solutions architects, project managers, and data scientists. They must be self-directed and comfortable supporting the data needs of multiple teams, systems, and products. The right candidate will be excited by the prospect of optimizing or re-designing our customers’ data architecture to support our next generation of products and data initiatives, and machine learning systems. Responsibilities - Keep our customers’ data separated and secure to meet compliance and regulations requirements. - Design, Build and Operate the infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources using SQL and cloud (mainly AWS) migration and ‘big data’ technologies. - Optimize various RDBMS engines in the cloud and solve customers' security, performance, and operation problems. - Design, Build and Operate large, complex data lakes that meet functional / non-functional business requirements. - Optimize various data types ingestion, storage, processing, and retrieval from near real-time events and IoT to unstructured data as images, audio, video and documents, and in between. - Use Jupyter Notebooks to build and deploy ML models. - Leverage AWS AI/ML pre built solutions to accelerate work for customers. - Work with customers and internal stakeholders, including the Executive, Product, Data, Software Development, and Design teams, to assist with data-related technical issues and support their data infrastructure and business needs. Qualifications - 3+ years of experience in a Data Scientist/Machine Learning Engineer role. - Bachelor's (Graduate preferred) degree in Computer Science, Mathematics, Informatics, Information Systems, or another quantitative field. - Experience with big data tools: Spark, ElasticSearch, Hadoop, Kafka, Kinesis etc. - Experience with relational SQL and NoSQL databases, such as MySQL or Postgres and DynamoDB or Cassandra. - Experience with AWS cloud services: EC2, RDS, EMR, Redshift etc. - Experience with functional and scripting languages: Python, Java, Scala, etc. - Experience with various ML models for classification, scoring and more. - Experience with Deep Learning Neural Networks (Convolution, NLP etc.) - Experience with AWS AI/ML Services. - Experience with Python coding. - Advanced working SQL knowledge and experience working with relational databases, query authoring (SQL) as well as working familiarity with a variety of databases. - Experience building and optimizing ‘big data’ data pipelines, architectures and data sets. - Strong analytic skills related to working with unstructured datasets. - Build processes supporting data transformation, data structures, metadata, dependency and workload management. - Working knowledge of message queuing, stream processing, and highly scalable ‘big data’ data stores. - Experience supporting and working with external customers in a dynamic environment. Certifications - AWS Machine Learning Specialty (Strongly Preferred) - AWS Solutions Architect - Associate (Strongly Preferred) Benefits - Our team inspires progress in each other and in our customers through our relentless pursuit of excellence; you will work with leaders who promote learning and personal development.

• Train, evaluate, and iterate on ML models and agentic systems for customer feedback, including owning our custom fine-tuning pipelines. Run experiments end-to-end, track results rigorously, and make clear recommendations on what to ship, iterate, or retire. • Build and maintain LLM-powered features: retrieval pipelines, reranking systems, insight agents, data mining agents, and automated taxonomy generation. • Design and run robust evaluation frameworks: build test sets, define metrics, evaluate non-deterministic systems, handle class imbalance, and automate checkpoint comparisons. • Improve and extend semantic search and retrieval, evolving from embedding-based approaches toward more advanced methods. • Write production-quality code and collaborate closely with Engineering on productionisation, model serving, data pipelines, and monitoring. • Work with Product and Commercial teams to translate business needs into practical ML solutions, and support client evaluations and accuracy benchmarking. • Mentor team members, review code and research, and bring relevant advances from the literature into the product.

• Lead the evolution of the Interactive Learning system's recommendation engine • Build and operate applied models for continuous improvement • Define measures of recommendation quality and partner across various teams for integration • Ship incremental improvements and enhance existing recommender features