The posting
AI Engineer
Location: Virginia
Employment Type: Full-Time
About the Company
Minfy is a technology consulting company that helps enterprises solve business problems with AI. Minfy’s advanced engineering capability enables enterprises to build intelligent applications, modernize legacy systems and operate mission-critical applications. Minfy combines cloud, data, and AI engineering capabilities with systems knowledge, governance frameworks and industry expertise to deliver solutions that enable faster time to market, lower cost, and better competitive differentiation for customers. Minfy was a Leader and Star Performer in the Everest Group’s AWS Services PEAK Matrix® Assessment 2025 and a Seasoned Vendor in AIM Research’s PeMa Quadrant for GenAI Service Providers 2026. Minfy is a premier tier consulting partner of AWS and has certifications and competencies across Anthropic, AWS, Microsoft, and Google Cloud. Founded in 2016, Minfy serves Fortune 500 companies, large enterprises, high-growth businesses, and startups across the world.
About the Role
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.
Compensation
The base salary range for this position is $180,000 – $200,000 USD annually, depending on experience, qualifications, and internal equity. This range reflects the anticipated base pay for this role and does not include potential bonuses, benefits, or other forms of compensation.
Equal Employment Opportunity
Minfy is an Equal Opportunity Employer. We are committed to creating an inclusive environment for all employees and applicants, and we do not discriminate on the basis of race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, disability, genetic information, veteran status, marital status, or any other characteristic protected by applicable federal, state, or local law. All qualified applicants will receive consideration for employment without regard to these characteristics.
Recruiting Fraud Notice: Minfy Technologies never requests payment, banking details, or personal financial information as part of our recruitment or hiring process. All official communication regarding job opportunities at Minfy will come from an email address ending in @minfytech.com. If you receive a suspicious job offer claiming to be from Minfy, or are asked to make any payment or share sensitive personal/financial information, please do not respond and report it to us immediately at [email protected]. Minfy is not responsible for any loss resulting from fraudulent job offers made in our name.
