AI Engineer

Minfy Technologies; United States; On-site; USD 150,000 - 230,000; Full time

Job summary

Minfy Technologies is seeking an AI Engineer to lead a personalization and ranking initiative for a large-scale consumer marketplace. You will set the technical direction, make key modeling decisions, and stay hands-on through production-readiness.

You will act as a senior technical contact with the customer, designing a structured PoC to measure lift from GenAI-based profiles, and own the deep-learning ranking model, embeddings, and feature integration.

Qualifications

• 10+ years in applied ML/data science with recommender systems or large-scale personalization.
• Production experience with LLMs, embeddings, and feature/profiles integration.
• Experience enabling customer-facing technical leadership and AI deployments.

Responsibilities

• Own technical strategy for personalization in a production ranking system and guide modeling decisions.
• Stay hands-on: build features, train models, run experiments, and write critical code.
• Set technical bar, mentor engineers, and review designs.
• Serve as senior technical point of contact with customers, communicating risks and progress.
• Design and run a PoC program comparing GenAI profiles vs. traditional features.
• Engineer user-level features from large behavioral data and integrate LLM-generated profiles.
• Own deep-learning ranking model with multi-task architectures, hyperparameter tuning, and bias correction.
• Define offline evaluation framework (NDCG, MRR, Precision/Recall at K) and analyze cohorts.
• Plan production path: serving, scheduling, shadow testing, and A/B readiness.

Skills

Recommender systems

Learning-to-rank

Large-scale personalization

LLMs in production

PyTorch

TensorFlow

Python

AWS

MLOps

Mentoring engineers

Communication with stakeholders

Education

Advanced degree in CS/ML

Tools

Amazon Bedrock

Job description

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.
• 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 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.
• 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