• Develop, test, and implement software solutions in Python and Java. • Collaborate with cross-functional teams to define, design, and deliver new features and products. • Maintain and improve the performance of existing applications. • Identify and fix bugs and performance issues. • Actively participate in code reviews and ensure the quality of produced code. • Document processes and results, contributing to knowledge retention within the team.

• Build and operate backend systems that serve AI-powered insurance workflows in production. • Design and implement AI orchestration layers that connect models, APIs, workflows, and business logic. • Build inference pipelines for LLM-based and AI-assisted automation systems. • Optimize latency, throughput, and cost across AI services (caching, batching, streaming, routing). • Design stable service boundaries between backend systems, ML components, and product APIs. • Implement observability: logging, metrics, tracing, alerting, and incident response workflows. • Debug production issues across distributed AI systems and resolve root causes. • Collaborate closely with frontend, product, operations, and ML teams to ship end-to-end features. • Continuously improve system reliability, scalability, and performance.

• Design and implement core Back-End services for real-time bidding, optimization, and ad delivery workflows • Build custom bidding agents for real-time bid decisioning • Develop high-load, low-latency components supporting real-time auction and delivery mechanics • Implement proprietary pacing, bid-shading, and optimization logic to improve campaign efficiency • Design and integrate forecasting capabilities for impressions, reach, outcomes estimation, and campaign planning • Work with OpenRTB, JSON, protobuf, and other ad-tech protocols and data formats • Collaborate with product, engineering, data, and ad operations stakeholders to deliver scalable performance advertising capabilities

• Architecting an AI-first GTM ecosystem that bridges the gap between Sales, RevOps, Marketing, and Customer Success. • Design, code, and deploy custom AI integrations from scratch to connect our CRM, Sales Engagement platforms, and other technology stack components. • Build proprietary internal LLM-based tools to automate account and contact enrichment, outbound messaging for early and middle of the funnel prospects. • Develop agentic workflows that handle routine administrative tasks (e.g., CRM data, meeting summaries, and follow-up scheduling) across the revenue organization. • Partner with RevOps and Marketing Ops to audit the existing tech stack, identifying redundancies where AI can replace high-cost, low-utility third-party vendors. • Implement predictive modeling to identify high-intent pipelines and provide Sales with real-time 'Next Best Action' recommendations. • Ensure data integrity across all platforms, creating a 'single source of truth' that AI models can leverage for accurate forecasting. • Collaborate with Customer Success to build AI-driven retention loops and automated renewal triggers. • Consult with Marketing leadership on AI-driven content distribution and performance marketing automation to lower Customer Acquisition Cost (CAC). • Act as an internal consultant, training GTM teams on how to effectively 'prompt' and utilize deployed AI tools to maximize their individual output.