AI Engineer

We are hiring an AI Engineer to be the lead technical contributor on a personalization and ranking engagement for a large-scale consumer marketplace. You will set the technical direction, make the key modeling decisions, and stay hands-on throughout. You will be a senior technical 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 profiles into a deep-learning ranking model, and driving the work from offline validation through production-readiness.

What You'll Do:

• Own the technical strategy for a personalization program on a production recommendation/ranking system, making the architecture and modeling decisions and being accountable for the results.

• Stay hands-on: build the features, train the models, run the experiments, and write the critical 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 expectations through ambiguity.

• Design and run a structured, parallel-track proof-of-concept that measures the incremental lift 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, recencyfrequency signals).

• Integrate LLM-generated user profiles into ranking models, including embedding generation, projection-layer tuning, gating, and ablation to ensure the signal is properly weighted.

• Own the deep-learning ranking model (multi-task CTR/CVR architectures such as sharedbottom MTL), including feature integration, hyperparameter optimization (Bayesian/grid search), 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 the customer's teams can operate and iterate independently after handoff.

• 10+ years in applied machine learning / data science, with deep hands-on experience in recommender systems, learning-to-rank, or large-scale personalization.

• Practical experience building with LLMs in production: generating and integrating modelderived features or profiles, working with embeddings, and reasoning about evaluation, latency, and cost.

• Experience with Amazon Bedrock or comparable managed LLM platforms for production inference.

• Hands-on experience with segment- or cohort-based personalization, including measuring performance 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 results clearly to engineers, data scientists, and senior business stakeholders, and to manage expectations through ambiguity.

• Customer-facing or stakeholder-facing experience: building trust, navigating competing priorities, and serving as a senior technical voice in high-stakes conversations.

• A track record of technical leadership through mentoring engineers, driving design decisions, and setting standards.

• Strong track record taking ML models from experimentation to production, owning the offline-to-online validation story (ranking metrics, ablations, segment analysis, shadow testing, A/B readiness).

• Deep, hands-on expertise in deep learning for ranking/recommendation - multi-task learning, embedding-based architectures - with a major framework (TensorFlow or PyTorch).

• Strong feature engineering on large behavioral datasets using the modern data stack (PySpark, SQL, distributed data lakes).

• Rigorous experimental methodology - hyperparameter optimization, bias correction, and a disciplined, hypothesis-driven approach to measuring true lift.

• Hands-on AWS experience across the ML lifecycle, and strong proficiency in Python., * Experience personalizing ranking for marketplaces or consumer platforms at scale (ecommerce, food delivery, media, or similar).

• MLOps maturity: model versioning, monitoring, and reproducible training pipelines.

• Advanced degree in Computer Science, Machine Learning, Statistics, or a related quantitative field.

• Prior experience in a client-facing consulting or professional-services delivery environment.