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, recency-

frequency 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 shared-

bottom 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.

Required Qualifications• 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 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 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.

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 related

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

environment.