AI Engineer

We are hiring an AI Engineer to be the lead technical contributor on a personalization andranking engagement for a large-scale consumer marketplace. You will set the technicaldirection, make the key modeling decisions, and stay hands-on throughout. You will be a seniortechnical point of contact with the customer — explaining trade-offs, managing expectations,and turning results into clear recommendations. You will lead a rigorous, POC-first program:engineering user-level features from behavioral data, integrating LLM-generated user profilesinto a deep-learning ranking model, and driving the work from offline validation throughproduction-readiness.What You’ll Do• Own the technical strategy for a personalization program on a productionrecommendation/ranking system, making the architecture and modeling decisions andbeing accountable for the results.• Stay hands-on: build the features, train the models, run the experiments, and write thecritical code.• Set the technical bar and support other engineers through design reviews, mentorship,and pairing.• Act as a senior technical point of contact with the customer, communicating progress,risks, and results to both engineers and senior stakeholders, and managing expectationsthrough ambiguity.• Design and run a structured, parallel-track proof-of-concept that measures the incrementallift of GenAI-based profiles over well-engineered behavioral ML features.• Engineer user-level features from large-scale behavioral data (category/product affinity,time-of-day and price-sensitivity patterns, per-user click/conversion history, recency-frequency signals).• Integrate LLM-generated user profiles into ranking models, including embeddinggeneration, projection-layer tuning, gating, and ablation to ensure the signal is properlyweighted.• Own the deep-learning ranking model (multi-task CTR/CVR architectures such as shared-bottom MTL), including feature integration, hyperparameter optimization (Bayesian/gridsearch), and bias correction (position/popularity).• Define and run the offline evaluation framework — NDCG, MRR, Precision/Recall at K —with segment-level analysis and ablation studies across user cohorts.• Establish the path to production: model serving and scheduled inference integration,shadow-mode testing, A/B framework readiness, and guardrail metrics.• Deliver clear technical documentation and lead knowledge-transfer sessions so thecustomer’s teams can operate and iterate independently after handoff.Required Qualifications• 10+ years in applied machine learning / data science, with deep hands-on experience inrecommender systems, learning-to-rank, or large-scale personalization.• Practical experience building with LLMs in production: generating and integrating model-derived features or profiles, working with embeddings, and reasoning about evaluation,latency, and cost.• Experience with Amazon Bedrock or comparable managed LLM platforms for productioninference.• Hands-on experience with segment- or cohort-based personalization, including measuringperformance at the segment level rather than relying on aggregate metrics.• Experience designing cold-start strategies for users or items with limited history.• Strong communication skills — able to explain modeling decisions, trade-offs, and resultsclearly to engineers, data scientists, and senior business stakeholders, and to manageexpectations through ambiguity.• Customer-facing or stakeholder-facing experience: building trust, navigating competingpriorities, and serving as a senior technical voice in high-stakes conversations.• A track record of technical leadership through mentoring engineers, driving designdecisions, and setting standards.• Strong track record taking ML models from experimentation to production, owning theoffline-to-online validation story (ranking metrics, ablations, segment analysis, shadowtesting, A/B readiness).• Deep, hands-on expertise in deep learning for ranking/recommendation — multi-tasklearning, embedding-based architectures — with a major framework (TensorFlow orPyTorch).• Strong feature engineering on large behavioral datasets using the modern data stack(PySpark, SQL, distributed data lakes).• Rigorous experimental methodology — hyperparameter optimization, bias correction, anda disciplined, hypothesis-driven approach to measuring true lift.• Hands-on AWS experience across the ML lifecycle, and strong proficiency in Python.Preferred Qualifications• Experience personalizing ranking for marketplaces or consumer platforms at scale (e-commerce, food delivery, media, or similar).• MLOps maturity: model versioning, monitoring, and reproducible training pipelines.• Advanced degree in Computer Science, Machine Learning, Statistics, or a relatedquantitative field.• Prior experience in a client-facing consulting or professional-services deliveryenvironment.

Experience: 10+
Posted: 2026-07-29T12:26:11.03Z