• Identify emerging AI tools and platforms relevant to legal services • Conduct structured research across vendors, technologies, and use cases • Evaluate tools based on ROI, scalability, compliance, and integration capability • Stay current on advancements in AI, automation, and legal technology • Design and execute pilot programs for selected AI tools • Build workflows and SOPs for deployment across departments • Coordinate implementation across teams (legal, intake, marketing, operations) • Ensure proper integration with existing systems (CRM, case management, communication tools) • Implement AI tools for lead generation, ad optimization, and content creation • Improve conversion rates through AI-driven targeting and messaging • Deploy AI for call handling, chatbots, and lead qualification • Optimize intake workflows to reduce response time and increase signed cases • Implement AI solutions for document automation, OCR, and data extraction • Reduce manual handling and improve turnaround times • Optimize workflows using AI for task automation, reminders, and case tracking • Improve efficiency and reduce case cycle time • Utilize AI for legal research, deposition summaries, and document review • Assist attorneys in trial prep through automation and data organization • Implement AI tools for billing, forecasting, collections, and financial reporting • Improve visibility into case profitability and firm performance • Establish and monitor KPIs across all implemented AI systems • Build dashboards and reporting systems to track performance and ROI • Continuously optimize tools based on data insights

• Design and train large scale transformer and hybrid foundation models • Own model architecture choices across text, multimodal, and emerging paradigms • Diagnose and resolve training instabilities at scale • Navigate scaling tradeoffs across data and compute • Apply strong fundamentals in deep learning and representation learning • Build models from first principles, not just adapt pre-existing codebases • Own optimiser and scheduler choices, including AdamW, Lion, Adafactor • Design and experiment with loss functions including next-token prediction and contrastive objectives • Design and execute large-scale training using FSDP, ZeRO-3, Tensor parallelism, Pipeline parallelism • Apply mixed precision (bf16, fp8) and gradient checkpointing

• Knowledge Graph Design & Construction: Architect and evolve per-tenant knowledge graph schemas, including entity resolution, temporal modeling, and ontology design tailored to sales execution domains. • Information Extraction: Architect NLP pipelines that extract structured knowledge from unstructured conversational and document data (sales calls, emails, CRM notes), including coreference resolution, relation extraction, and event detection. • Contextual Reasoning & Recommendation: Design reasoning and inference layers over the knowledge graph to power next-best-action suggestions, deal risk scoring, coaching recommendations, and competitive intelligence surfaces. • Representation Learning: Design and train graph-based models (GNNs, relational embeddings, link prediction) over heterogeneous, multi-relational graph structures to support downstream reasoning and retrieval tasks. Diagnose and address embedding quality issues including cold-start entities, and temporal drift. • Domain Modeling: Formalize sales execution concepts such as deal stages, buyer engagement patterns, rep behaviors, and account health, into structured representations that ground the platform's AI capabilities. Extract ontology structure. Lead ontology versioning and migration. • Cross-functional Collaboration: Partner with engineering, product, and data teams to bring models from prototype to production, ensuring reliability and measurable impact at scale.

• Knowledge Graph Design & Construction: Design and implement entity resolution and ontology population within established graph schemas. Write and optimize queries for graph traversal and feature extraction. Own data quality for assigned domains. • Information Extraction: Build pipelines that extract structured knowledge from unstructured conversational and document data (sales calls, emails, CRM notes), including coreference resolution, relation extraction, and event detection. Run experiments to compare approaches and improve accuracy metrics. • Contextual Reasoning & Recommendation: Implement graph traversal logic and feature queries that feed downstream scoring signals. Build and maintain features for deal risk, next-best-action, or coaching recommendation surfaces. • Representation Learning: Train and evaluate link prediction and node classification models using established graph embedding methods. Implement evaluation pipelines and track model performance over time. • Domain Modeling: Translate sales concepts, such as deal stages, buyer engagement patterns, rep behaviors, and account health, into graph nodes and relationships under the guidance of senior scientists. Contribute to ontology design and documentation. • Cross-functional Collaboration: Work with software engineers to deploy models and pipelines into production. Write clean, tested code. Monitor system health and respond to incidents. Participate in code review and design discussions.