• Own and evolve the hybrid search pipeline – lexical retrieval, dense vector search, reciprocal rank fusion, and multi-stage reranking on Elasticsearch • Build and train adaptive retrieval models – lightweight classifiers and ranking models that configure search behavior per query, per category, per context (source routing, per-attribute boost prediction, filter mode decisions) • Design and productionize the learning-to-rank system – from feature engineering through model training (LightGBM, ONNX) to production deployment and A/B evaluation • Build the search feedback loop – instrument and integrate behavioral signals (CTR, conversions, add-to-cart) into ranking and retrieval as features for LTR, reward signals for adaptive retrieval, and inputs for search-side personalization • Build the business and ordering layer – separating organic relevance from sponsored/partner placement with quality gates, slot allocation, campaign configuration, and an auction-style approach as the system matures • Own the offline enrichment pipeline – LLM-based product enrichment at scale, data quality monitoring, and index management • Instrument and evaluate everything – bulk evaluation pipelines, per-category metric tracking, regression detection, experiment analysis • Integrate query understanding outputs into retrieval – translating extracted attributes, intents, and constraints into filters, boosts, and retrieval strategy decisions

• Collaborate with the team using Confluence, Discourse, GitLab, and Jira • Perform code reviews and maintain high engineering standards • Work with government leadership to ensure program alignment • Meet with customers to collect requirements and incorporate feedback • Provide regular progress updates to leadership and customers • Conduct multidisciplinary research to support modeling and development goals • Contribute to team vision, direction, and simulation architecture discussions • Design, develop, document, test, and debug space simulation software

• contribute to designing, scaling/building, evaluating, integrating, shipping, and refining reward signals for recommendations by hands-on ML development • promote and role-model best practices of ML systems development, testing, evaluation • lead collaborations and align across PZN to integrate and A/B test mid-term signals in various recommendation systems

• Own the technical build of the first-generation Trustbridge supplier app end-to-end • Lead and mentor a team of junior engineers and interns based in India • Translate business and product specifications into technical plans • Collaborate with the founder and Fractional CTO on architecture decisions