Role Description As one of the founding members of Prolific's newly formed AI Data Services team, you'll help build the quality systems behind some of the world's most advanced AI models. Data quality is a strategic priority for Prolific, so this is a high-visibility role with direct exposure to senior stakeholders. This isn't a traditional QA role; we are looking for an innovative thinker that can leverage their expertise to define what good means where no definition exists yet. Your primary focus is working directly with clients and alongside frontier AI labs, translating what their models need into robust human data and evaluation strategies. Rather than checking quality at the end of a project, you'll engineer quality into every stage of the lifecycle, from study design and participant strategy through to evaluation, launch readiness, and client delivery. You will also work alongside our product engineering and supply teams to define and build the quality infrastructure that will enable us to deliver high-quality human data at scale. Much of the work you'll tackle won't have an existing playbook; you'll help create it. What You’ll Be Doing - Design the quality frameworks that underpin complex human data programmes, from evaluation rubrics through to launch readiness. - Work with clients as a strategic thought partner, challenging annotation schemas and data requirements when they won't produce the signal the model needs. - Advise on project design and how the choice of schema can impact data quality. - Build quality upstream across the operational workflow, from recruitment, screening, and training through to writing guidelines and running calibration sessions. - Build scalable quality systems, measurement frameworks, and automated checks using Python and SQL. - Partner with product and engineering to build the quality infrastructure that delivers high-quality human data at scale. - Investigate data quality and integrity issues, identifying root causes and turning insights into scalable improvements. - Architect and build dashboards, monitoring, and reporting that provide clear visibility into quality and operational performance. - Raise the quality capability across the company, upskilling operations and acting as a thought mentor to junior analysts. - Help define how Prolific approaches quality across new AI domains, shaping best practice as the team grows. Qualifications - 5+ years of experience in building quality, evaluation, or annotation systems within AI, machine learning, LLMs, or human data environments. - Strong Python and SQL skills, with a passion for using data to solve complex quality problems. - A solid understanding of machine learning pipelines and how human data impacts model performance. - Strong analytical and statistical thinking, with experience designing scalable quality frameworks. - The confidence and credibility to interact with stakeholders at frontier labs and act as a partner. - The ability to leverage your experience and expertise to influence and guide stakeholders at every level, both client-side and internally. - The ability to turn your own data analysis and quality methodology into requirements that product and engineering can build into systems. - The ability to explain your data analysis and findings clearly to non-technical stakeholders, so they can act on them. - A proactive, builder's mindset - you enjoy creating new systems, navigating ambiguity, and improving how things work. Requirements - Experience with LLM evaluation, RLHF, AI safety, or red teaming. - Experience translating vague model or evaluation goals into clear annotation specifications. - Experience working with human annotation programmes or human data operations. - Familiarity with calibration, inter-rater agreement, drift detection, or other evaluation methodologies. Benefits - Competitive salary, benefits, and remote working within an impactful, mission-driven culture. - Compensation packages for eligible roles include base salary, equity, and benefits. - Opportunity to earn a cash variable element, such as a bonus or commission. - Salary range reflects the minimum and maximum target for new hires, based on the role’s location as well as skills, experience, and relevant education or training.

• Architect Enterprise‑Scale Data Solutions: Design, build, and evolve high‑volume batch and real‑time data pipelines using PySpark, SparkSQL, Databricks Workflows, and distributed processing frameworks. • Own Platform‑Level Integrations: Develop end‑to‑end ingestion and transformation frameworks integrating Databricks, Snowflake, AWS services (such as S3, SQS, Lambda), and external data provider APIs, with a strong focus on data quality, lineage, and schema evolution. • Lead Technical Design for Clients: Serve as the technical lead for complex client implementations, defining highly available, fault‑tolerant architectures across multi‑account cloud environments. • Translate Business Needs into Architecture: Convert complex business and regulatory requirements into scalable technical designs, detailed specifications, and reusable engineering patterns. • Set Engineering Standards: Establish and champion best practices across CI/CD, code quality, testing, orchestration, monitoring, logging, and observability for data platforms. • Ensure Security & Compliance: Design and implement security‑first data solutions, including RBAC, encryption, PHI handling, auditability, and alignment with HIPAA and SOC 2 requirements. • Optimize Performance & Cost: Profile and tune compute workloads, cluster configurations, partitioning strategies, indexing, and caching across Databricks and Snowflake environments. • Provide Technical Mentorship: Mentor senior and junior engineers, conduct design and code reviews, and raise the overall technical bar across teams. • Produce Technical Artifacts: Create clear documentation, including architecture diagrams, runbooks, and operational standards that support scalable delivery.

Role Description A Deal Group é um ecossistema de tecnologia orientado a entregas end-to-end, que integra soluções especializadas em desenvolvimento digital (Deal Tech), marketing de experiência (Alot), cloud e Devops (O2B), inteligência de dados e IA (Sysvision) e soluções financeiras (baasic). Através de um modelo colaborativo e versátil, o grupo combina essas competências para resolver desafios complexos, oferecendo desde serviços pontuais até transformações digitais completas e escaláveis, garantindo que a expertise técnica de cada unidade de negócio culmine em uma execução única e estratégica para o negócio dos nossos clientes. - Desenhar e evoluir arquiteturas de dados: Definir padrões, boas práticas e estratégias que garantam eficiência e escalabilidade, incluindo FinOps, governança e integração entre plataforma de IA (ML/GenAI). - Viabilizar iniciativas de dados: Atuar desde a concepção dos projetos, interagindo com áreas de negócio para entender necessidades e traduzir requisitos funcionais em soluções técnicas robustas. - Criar soluções para ingestão e tratamento de dados: Desenvolver pipelines resilientes e escaláveis, aplicando conceitos de DataOps para garantir qualidade, automação e monitoramento contínuo. Qualifications - Experiência sólida em engenharia de dados em ambientes de grande escala. - Proficiência em arquitetura e modelagem de dados analíticos (Data Lake, Data Warehouse, Lakehouse, Data Mesh). - Domínio de tecnologias de Big Data e processamento distribuído (Spark). - Conhecimento avançado em SQL e otimização de consultas complexas. - Programação avançada em Python e pySpark. - Experiência com serviços de dados em nuvem AWS (Glue, EMR, Lake Formation, Redshift, S3, Step Functions, etc) e práticas de DataOps e FinOps. - Experiência com pipelines ETL/ELT (dbt, Glue) e orquestração (Airflow, Step Functions). - Familiaridade com microsserviços, APIs, containers e mensageria (Kafka, Kinesis). - Conhecimento em governança, segurança e compliance em ambientes críticos. - Boas práticas de engenharia de software (versionamento, CI/CD, testes). - Capacidade de traduzir conceitos de negócio em métricas quantitativas (KPIs/OKRs). - Experiência em mentoria técnica e liderança de times. Requirements - Atuação prévia em ambientes de IA (preparação de dados para ML/GenAI). Benefits - Trilha de carreira; - Convênio Farmácia; - Totalpass; - Assistência Médica; - Incentivo a certificação e qualificação; - Cultura Deal.

Role Description We are building a next-generation AI Payments Engineering Team to design and deliver an AI-native Payments Operating System. This team will combine AI engineering, payments domain expertise, and production automation to transform high-effort, high-risk financial operations into scalable, intelligent, and auditable systems. The team will focus on automating critical Payments processes such as: - Least cost routing - Reconciliations - Audit - Incident automation The mission is to deliver efficiency with control integrity—not just automation speed. Key Responsibilities - Assess and map end-to-end Payments operations, including: - Reconciliations - Reporting & controls - Audit - Identify operational bottlenecks, capability gaps, and integration failures. - Define a Payments automation roadmap based on operational impact, regulatory risk, and scalability. - Design the AI-native Payments Operating System, including: - Agentic system design - Data pipelines - Integration with financial systems - Control and reporting frameworks - Design and implement an agentic Payments layer that: - Operates alongside the current stack - Automates workflows today - Enables gradual migration to a future-state architecture - Evaluate and simplify the existing Payments technology stack. - Represent Payments in the AI Center of Excellence, ensuring alignment across teams. - Classify all automation by risk tier before development. - Ensure each solution includes: - Named Process Owner - Documented data flows - Access controls - Full audit logging - Design automation that preserves control integrity in regulated environments. - Develop reusable frameworks, templates, and workflows. - Create documentation and standards to scale adoption. - Build dashboards and metrics to measure: - Efficiency gains - Cost reduction - Risk outcomes - ROI - Train Payments teams on AI-enabled workflows and tools. - Work closely with: - Payments Backend teams - Infrastructure & platform teams - Compliance & audit teams - AI Center of Excellence - Translate business and finance requirements into technical solutions and roadmaps. - Communicate effectively with technical and non-technical stakeholders. Qualifications - Hands-on experience deploying production-grade AI/agentic systems. - Strong understanding of: - LLM orchestration - Multi-agent systems - AI failure modes in high-stakes environments - Proficiency in: - Spring AI/Python-based orchestration - Workflow engines (n8n or equivalent) - AI APIs (Anthropic/Claude or similar) - Ability to deliver across the lifecycle: Discovery → Design → Build → Deploy → Optimize. - Strong architectural thinking for scalable, future-ready platforms. Ways of Working - Operates effectively in fast-paced, ambiguous environments. - Strong ownership mindset with a bias for execution. - Ability to bridge business and engineering domains.