Software Engineering Lead

LexisNexis is a leading global provider of legal, regulatory and business information, and analytics. The Software Engineering Lead will serve as a hands-on technical leader and people manager for a team focused on building, deploying, and scaling AI-powered features, ensuring robust and scalable solutions aligned with business goals.

Responsibilities:

• Lead a team of Data Scientists / Applied AI Engineers responsible for delivering production AI capabilities that improve products, workflows, and customer outcomes
• Serve as the initial point of escalation for technical and delivery issues within the team’s area of responsibility
• Contribute directly to hands-on technical work, including solution design, coding, experimentation, model development, feature implementation, and production troubleshooting
• Partner with product, engineering, design, and business stakeholders to define requirements, prioritize opportunities, and translate business needs into deployable AI solutions
• Drive delivery of AI/ML features from concept through launch, ensuring solutions are robust, scalable, maintainable, and aligned to business goals
• Establish and uphold best practices for experimentation, model evaluation, deployment, monitoring, and ongoing optimization of AI systems in production
• Work closely with engineering teams to operationalize models and AI services, including APIs, batch pipelines, real-time inference, and human-in-the-loop workflows where applicable
• Help the team focus on pragmatic execution by balancing innovation with speed, reliability, cost, explainability, and operational constraints
• Define and monitor success metrics for shipped features, and use feedback, telemetry, and experimentation results to drive continuous improvement
• Write and review technical specifications, implementation plans, and design documents for moderately to highly complex AI-enabled systems
• Resolve complex technical issues involving data, modeling, integration, deployment, and production performance
• Mentor and develop team members in both technical execution and business impact, ensuring they are equipped to build and ship high-value AI features
• Keep abreast of developments in applied AI, machine learning, LLMs, and adjacent technologies, and identify practical opportunities to apply them
• Carry out management responsibilities in accordance with organizational policies and applicable laws, including interviewing, hiring, training, performance management, coaching, recognition, and addressing employee concerns

Requirements:

• Strong experience building and shipping AI/ML-powered product features in production environments, with an emphasis on implementation, iteration, and measurable outcomes over research prototypes
• Advanced knowledge of machine learning development lifecycles, including problem framing, data preparation, model development, evaluation, deployment, monitoring, and continuous improvement
• Experience applying techniques such as predictive modeling, classification, ranking, recommendation systems, NLP, LLM-powered workflows, or other statistical/ML methods to real business problems
• Strong proficiency in Python and common data science / machine learning libraries and frameworks
• Experience partnering closely with software engineering teams to productionize models and integrate AI capabilities into scalable applications and services
• Strong knowledge of software development best practices, including version control, testing, code review, CI/CD, and agile delivery methodologies
• Experience designing experiments, defining success metrics, and using offline and online evaluation methods to assess feature performance
• Strong understanding of data modeling, data quality, feature engineering, and working with structured and unstructured data
• Proficiency with SQL and data manipulation techniques, including performance optimization
• Experience with modern data and ML tooling, including cloud platforms, model serving, orchestration, monitoring, and MLOps practices
• Ability to guide build-vs-buy decisions and effectively leverage third-party AI platforms, foundation models, or vendor capabilities where appropriate
• Strong problem-solving skills, including the ability to translate ambiguous business needs into practical technical solutions
• Ability to write and review detailed technical designs, model documentation, and implementation plans for complex systems
• Strong skills in setting, communicating, implementing, and achieving business objectives through direct management of others