Top Tech Jobs & Startup Jobs

Leads architecture, implementation, and scaling of production ML and LLM systems for legal products. Builds RAG pipelines, agentic applications, hybrid search, APIs, microservices, data pipelines, and model-serving infrastructure. Establishes reusable AI frameworks, deployment and observability practices, and CI/CD standards while optimizing latency, reliability, scalability, and cost. Partners with data scientists to productionize models and provides technical leadership in system design and engineering excellence.

Lead and mentor data scientists and analysts, develop predictive models and algorithms, conduct NLP and LLM experiments, design scalable data-driven solutions, and oversee statistical models and data architectures. Collaborate with cross-functional stakeholders to solve business problems, communicate actionable insights, and incorporate state-of-the-art data science methodologies while improving performance and reliability.

Reviews, edits, and audits AI-assisted legal opinions and case law content. Analyzes cited authorities, determines precedential relationships, applies history and treatment phrases, validates output against source material, and corrects legal or editorial issues. The role requires accurate, consistent work, strong legal judgment, issue spotting, deadline management, and adherence to editorial guidelines while working independently in a remote environment.

Resolve complex customer technical issues, implement tailored solutions, assess technical environments during pre-sales, and maintain relationships with key accounts. Collaborate with internal and external teams to troubleshoot and escalate issues, identify expansion opportunities, analyze trends, advocate for customers, and provide product improvement insights.

Designs, builds, and scales AI infrastructure and software supporting GenAI products. Responsibilities include managing model capacity, maintaining AI governance and security oversight, translating complex requirements into software designs, resolving complex technical issues, implementing coding best practices, conducting code reviews, and collaborating with and mentoring engineering and product teams.

Define positioning and messaging for SaaS and DaaS products; lead global go-to-market strategy and product launches; translate customer and market insights into commercial plans; support sales enablement and revenue growth; influence product strategy and collaborate with international teams.

Creates, develops, and maintains commercial legal practical guidance and current-awareness content. Monitors legal and market developments, produces news and analysis, engages with customers and legal professionals, supports practitioner queries, and collaborates across practice areas. The role requires an England and Wales-qualified lawyer with substantial commercial law experience, strong legal research and drafting skills, and the ability to communicate complex concepts clearly while meeting demanding deadlines.

Perform royalty operations and financial controls: identify control gaps, document policies and SOPs, analyze financial data for anomalies, calculate impacts, validate product/business logic (LexisNexis products), and drive remediation and risk-aware solutions across cross-functional teams.

Provides FP&A and commercial finance support for a full P&L, including budgeting, forecasting, performance analysis, business cases, pricing and deal evaluation, risk and opportunity assessment, and KPI monitoring. Partners with product, sales, marketing, operations, and senior finance stakeholders to translate financial results into actionable insights. Improves reporting and analysis through automation, generative AI, dashboards, scenario modeling, and enhanced financial processes.

Leads end-to-end development and production deployment of advanced AI and data science systems, including LLM, NLP, deep learning, RAG, and agent architectures. Designs scalable cloud pipelines, establishes model validation and governance practices, conducts experimentation, and monitors performance. Partners with product and business teams on risk, fraud, legal analytics, and compliance solutions; presents recommendations to leadership; mentors data scientists; and guides strategic data science initiatives.