AI Ranking Systems Engineer

@MashoodurRehman1

I am a seasoned AI engineer with extensive experience in building and optimizing AI ranking systems for enterprise applications. My expertise encompasses large-scale feature engineering and deploying robust machine learning models, which aligns with the requirements of your ranking systems project. I have a strong command of Python and Spark for data preparation and have implemented TensorFlow and PyTorch in production environments. My previous work has involved integrating GenAI/LLM methodologies to enhance recommendation logic. Notably, I successfully led a project where I designed a highly effective ranking model that improved CTR metrics by leveraging AWS services, including S3, EMR, and SageMaker, demonstrating clear performance improvements via offline (NDCG, MAP) and online A/B testing. I look forward to the possibility of discussing how I can contribute to your initiative. Please let me know if there is a convenient time for us to connect. I am ready to provide additional details on request.

@ZohaibRoy

⭐⭐⭐⭐⭐ Build Efficient Recommendation Systems for Enterprise Products ❇️ Hi My Friend, I hope you're doing well. I've reviewed your project needs and see you're looking for a senior-level engineer for your recommendation and ranking pipeline. Look no further; Zohaib is here to help you! My team has successfully completed 50+ similar projects in building recommendation systems. I will focus on large-scale feature engineering and EDA, ensuring that the models we create significantly improve your CTR and CVR metrics. ➡️ Why Me? I can easily handle your recommendation system project as I have 5 years of experience in Python and Spark, specializing in feature engineering, model tuning, and deployment. My expertise includes building custom neural networks and working with AWS tools like S3, EMR, and SageMaker. I also have a strong grip on TensorFlow and PyTorch, making me well-equipped for this role. ➡️ Let's have a quick chat to discuss your project in detail. I’d love to share samples of my previous work and how I can add value to your project. Looking forward to connecting! ➡️ Skills & Experience: ✅ Python ✅ Spark ✅ TensorFlow ✅ PyTorch ✅ AWS S3 ✅ AWS EMR ✅ AWS SageMaker ✅ Feature Engineering ✅ Model Tuning ✅ A/B Testing ✅ Data Analysis ✅ Neural Networks Waiting for your response! Best Regards, Zohaib

@Mahihassi

As a seasoned AI and Cloud Developer, I am well-versed in the particular requirements of the project you have outlined. With fluency in both Python and Amazon Web Services (AWS), my skill set holistically aligns with your prerequisites. Throughout my professional trajectory, I have built proficient backend architectures, robust APIs, and responsive web interfaces fitting for data visualization and control. All of these proficiencies are crucial in developing, tuning, and ultimately deploying custom models that shape CTR/CVR. In addition, my experience extends to designing AI-powered SaaS platforms and web dashboards that complement modern businesses like yours. My focus on clean architecture and scalability resonates with your need for a production-ready recommendation and ranking pipeline capable of delivering tangible results. Moreover, I understand the pertinence of reliable monitoring, online A/B tests, as well as demonstrating lift through clear offline metrics - skills which I have honed throughout numerous projects. To cite one applicable example from my portfolio, I recently spearheaded a project where we employed AWS SageMaker for packaging and deploying our custom algorithms. This led to a drastic enhancement in model performance due to diligent monitoring and optimization post-deployment.

@ahmadayaz

As a seasoned engineer specializing in feature engineering, model tuning, and production deployments for improving CTR/CVR, I bring expertise in Python, Spark, TensorFlow/PyTorch on AWS infrastructure. With a proven track record in developing feature stores, custom neural networks, and optimizing model performance, I am keen on integrating GenAI/LLM/RAG methods for ranking logic enhancements. Experienced in leveraging AWS services like S3, EMR, and SageMaker for end-to-end solutions, including a personalized recommendation system that boosted conversion rates by 25%, I am adept at improving NDCG, MAP, AUC metrics through robust A/B testing. I am excited about collaborating to enhance your recommendation and ranking pipeline and am eager to share my resume and LinkedIn profile for further discussion on how my experience aligns with your project requirements.

@tangramua

Dear ,   We carefully studied the description of your project and we can confirm that we understand your needs and are also interested in your project. Our team has the necessary resources to start your project as soon as possible and complete it in a very short time.   We are 25 years in this business and our technical specialists have strong experience in Python, Machine Learning (ML), Amazon Web Services, Big Data Sales, Hadoop, AWS SageMaker, Model Deployment, Model Tuning, GenAI, Retrieval-Augmented Generation (RAG) and other technologies relevant to your project.   Please, review our profile https://www.freelancer.com/u/tangramua where you can find detailed information about our company, our portfolio, and the client's recent reviews.   Please contact us via Freelancer Chat to discuss your project in details.    Best regards, Sales department Tangram Canada Inc.     

@Lats7

Hello, Your project aligns closely with my experience building production AI platforms, recommendation systems, RAG pipelines, and enterprise data processing solutions. I have over 10 years of software engineering experience and 4+ years delivering AI applications using Python, LangChain, LangGraph, TensorFlow, PyTorch, AWS, Azure AI, vector databases, and large-scale data pipelines. I've built end-to-end document ingestion and retrieval systems, feature engineering workflows, semantic search, and AI ranking solutions focused on improving business outcomes rather than just model accuracy. I'm comfortable working with Spark, SageMaker, S3, EMR, feature engineering, model optimisation, RAG architectures, and deploying monitored production endpoints. I focus on building scalable, maintainable systems with measurable improvements through offline evaluation and production monitoring. I'd be happy to share my CV, LinkedIn profile, and discuss relevant AI projects in more detail. Best Regards, Stefan

@PINESCRIPTERR

Hi, I can support the full recommendation lifecycle: large-scale EDA and feature engineering in Spark, reproducible training datasets, neural ranking models, offline evaluation, SageMaker deployment, monitoring, and controlled online experiments. My approach begins with event-quality and leakage audits, point-in-time correct feature generation, candidate retrieval, and a strong baseline. I would then iterate through learning-to-rank or deep retrieval/ranking architectures using NDCG, MAP, AUC, calibration, coverage, diversity, latency, and business constraints rather than optimising one metric in isolation. On AWS, the pipeline can use S3 for versioned datasets, EMR for Spark processing, SageMaker Pipelines and Training Jobs, Feature Store where appropriate, Model Registry, autoscaled endpoints, CloudWatch monitoring, and CI/CD promotion gates. Online testing will include guardrails for latency, revenue, user experience, and statistical validity. RAG can enrich candidate context or explanations, but I would keep final ranking deterministic and measurable unless experiments prove incremental lift. A verified end-to-end ranking case study, CV, LinkedIn profile, and supporting details can be provided privately during screening. Regards, Houssame

@JustinJcob

How are your current candidate generation and ranking stages split, and do you already have a feature store in place or should it be designed from scratch? For RAG in ranking, are you aiming to use semantic retrieval as a candidate source, a feature signal, or a re-ranking layer? I can help build your production-grade recommendation and ranking pipeline across Python/Spark, AWS S3/EMR/SageMaker, TensorFlow/PyTorch, feature engineering, offline evaluation, deployment, and monitoring. I’ve owned an end-to-end ranking workflow using S3 for data storage, EMR/Spark for feature generation, SageMaker for model training/deployment, and CloudWatch for endpoint monitoring, optimizing AUC/NDCG before A/B rollout. I’m young, a fast learner, available 24/7, and ready to move quickly on your CTR/CVR goals. Let’s chat so I can share my CV/LinkedIn and discuss the answers. Kind regards, Haroon Z.

@vishal1145

Your ranking pipeline will fail in production if your feature store cannot handle real-time updates during inference. Most teams discover this bottleneck after deployment when latency spikes kill CTR gains. Quick questions - what's your target p99 latency for inference, and are you planning cold-start handling for new items that lack historical engagement signals? Here is the architectural approach: - PYTHON + SPARK: Build distributed feature pipelines on EMR that precompute embeddings and aggregate signals at scale, then materialize to S3-backed feature store with sub-50ms lookup. - SAGEMAKER + MODEL TUNING: Deploy multi-armed bandit experiments using SageMaker endpoints with auto-scaling, A/B test candidate models against baseline using NDCG@10 and online CTR lift. - RAG + RANKING: Inject retrieval-augmented context into ranking layers by encoding user intent with sentence transformers, then re-rank top-K candidates using cross-encoder fine-tuned on your engagement data. I've built similar systems for 2 e-commerce platforms that improved CTR by 18-23% through hybrid neural/RAG architectures. Let's schedule a 20-minute technical call to walk through your feature engineering strategy and deployment constraints.

@demivision

Hi there, Employer, Thank you for sharing such a compelling and forward-thinking project. As an AI engineer with extensive experience architecting large-scale recommendation and ranking systems for enterprise clients, I’m excited about the opportunity to collaborate on your production-grade pipeline. I’m deeply familiar with every aspect outlined in your brief. My recent work involved owning the end-to-end design and deployment of a personalized ranking engine for a global e-commerce platform. I led feature engineering with PySpark, leveraged S3/EMR for distributed data processing, and built hybrid neural ranking models in PyTorch. We deployed to SageMaker endpoints with CI/CD, monitored prediction drift via CloudWatch, and measured success using NDCG and A/B online tests. Incorporating GenAI, I developed a RAG-powered module that blended user profile retrieval with LLM-generated ranking signals—significantly boosting CTR and downstream engagement. For your project, I propose starting with a robust feature store on AWS (SageMaker Feature Store or custom solution), ensuring reproducible training/validation splits. I’ll conduct thorough EDA with Spark, then iterate on model architectures—combining classic deep ranking with GenAI-driven re-ranking via RAG, tailored to your product needs. Deployment will focus on SageMaker endpoints, with automated monitoring and feedback loops for continual improvement. I’m passionate about not just building models, but delivering real, measurable lift in production. Please find my CV and LinkedIn profile attached for your review—I look forward to discussing your specific goals and challenges in detail. Best regards, DemiVision, LLC

@HamparProd

Hi, Your project is a great match for my background in production AI/ML systems, recommendation engines, and scalable cloud deployments on AWS. I've built end-to-end ML pipelines covering feature engineering, model training, evaluation, deployment, and monitoring using Python, PyTorch/TensorFlow, Spark, SageMaker, EMR, and S3. My approach is data-driven: establish a reliable feature store, build reproducible training pipelines, optimize ranking models against metrics such as NDCG, MAP, and AUC, then validate improvements through controlled A/B testing before production rollout. I'm also experienced with LLMs and RAG architectures, using retrieval to enrich ranking and recommendation quality while maintaining low-latency inference in production. One recent project involved building a personalized recommendation pipeline from feature engineering through SageMaker deployment, using S3, EMR, SageMaker, CloudWatch, and IAM for scalable training, inference, and monitoring. I'm available to start immediately and would be glad to discuss your roadmap.

@sekai1116

As an accomplished Senior Full-Stack, Mobile, and AI Engineer, I bring to the table the precise range of skills you're seeking for. My proficiency in Amazon Web Services, including AWS SageMaker paired with my expertise in Python, speaks directly to your project's needs. My long-standing experience in designing, building and maintaining modern applications will prove highly beneficial as we deploy your enterprise products on a large scale. Lastly, my focus on clean coding principles and performance optimization will ensure that we squeeze every millisecond and metric point out of model performance while packaging and deploying them for real-world use. I emphasize leveraging robust monitoring tools to ensure seamless deployments on AWS. My experience enables me to demonstrate lift through clear offline metrics (NDCG, MAP, AUC) and the use of online A/B tests for validation. Please find my latest CV and LinkedIn profile links for further details. I truly believe that my technical skills blended with a passion for delivering value-added solutions aligns perfectly with what you seek. I am excited about this opportunity to optimize your recommendation and ranking pipeline as it lines up closely with a project I successfully owned end-to-end using similar AWS tools. Let's make winning experiences together!

@sufyanasmat38

Hi, I have extensive experience building AI/ML systems, recommendation engines, ranking pipelines, feature engineering workflows, and production-grade model deployment on AWS. My background includes Python, Spark, TensorFlow, PyTorch, SageMaker, EMR, and large-scale data processing pipelines focused on improving business metrics through data-driven optimization. I have worked across the full ML lifecycle, including feature store design, training dataset creation, model development, hyperparameter tuning, offline evaluation, deployment, monitoring, and performance optimization. My experience also includes integrating modern AI techniques such as LLMs, retrieval-augmented generation (RAG), semantic search, and intelligent ranking systems to improve relevance and user engagement. I am comfortable owning projects end-to-end, from exploratory data analysis and feature engineering to SageMaker deployment and A/B testing. My focus is always on building scalable, maintainable systems that deliver measurable improvements in CTR, CVR, and ranking quality while maintaining strong engineering and monitoring practices. I would be happy to discuss your architecture, ranking objectives, and experimentation strategy in more detail. Please send me a message so we can explore the opportunity further.

@manishSuryaR

Hello! Bravion from Cleveland here My method to complete this project involves first designing a scalable feature store using Spark and Python to streamline feature engineering and ensure high-quality training data. I will then build and fine-tune custom neural networks with TensorFlow and PyTorch, integrating retrieval-augmented generation techniques to enhance ranking precision. Finally, I will deploy optimized models on SageMaker endpoints with comprehensive monitoring to maximize CTR/CVR improvements and ensure robust production performance. If you want high-quality results, please do not hesitate to contact me.

@Hubready

Hello!, This is James from Hollywood... I read your project description carefully, and I understand you’re rolling out a production-grade recommendation and ranking pipeline for enterprise use. That means this is not just “build an ML model” work, but a system that needs to be reliable, tunable, deployable, and scalable in AWS. I have 15+ years of experience across Python, ML, AWS, SageMaker, data pipelines, and production deployments. I’ve built ranking and retrieval-style systems, RAG workflows, model tuning flows, and cloud-based data processing pipelines that are designed to actually hold up in real use, not just in demos. My usual approach would be: 1. Clarify ranking goals, data sources, and success metrics 2. Review the current pipeline and identify gaps 3. Design the model/retrieval flow and deployment path 4. Implement, test, tune, and monitor for production stability Could you please clarify the following questions to help me better understand the project? 1. What is the main ranking objective, click-through, conversion, relevance, or a custom business score? 2. What data sources and volume are involved, and is the pipeline already running in AWS or starting fresh? 3. Do you need only model engineering, or also deployment, monitoring, and ongoing tuning? I’m the kind of person who pays attention to the details that usually decide whether these systems succeed or fail. If helpful, I can jump into a quick chat and map out the cleanest path forward.

@codeandcommand

I have done AWS SageMaker deployments and RAG pipelines before, and ranking systems on enterprise data are their own beast, especially once GenAI retrieval gets layered in. I can start today and have a working pipeline draft by Friday. The budget and timeline here are starting points based on the post, we will firm both up once we cover the full scope. Want to jump on a quick call?

@melopgabriel

Hi, your project calls for a senior engineer who can take ranking from feature engineering through production deployment and measurable lift. I’ve built and tuned recommendation systems that combine Python, Spark, TensorFlow, and PyTorch, with AWS-based pipelines on S3, EMR, and SageMaker. My focus would be on clean feature store design, strong offline evaluation, and a deployment path that is easy to monitor and improve. I also have experience adapting retrieval and generation methods into ranking workflows when they add value, rather than forcing them in. I would start by understanding your current data flow, defining the right training and validation sets, then iterating on model architecture, latency, and metric lift until the system is ready for SageMaker endpoints and A/B testing. I can share my latest CV and LinkedIn right away. Best regards, Gabriel

@Jurasicutomation

With over 8 years of experience as an AI Full-Stack Developer, I am uniquely positioned to meet the demands of your AI Ranking Systems project. My skills span across Machine Learning (ML) and Python — two critical tools for your project. Throughout my career, I have successfully transformed complex ideas into scalable, high-performing products that drive substantial business value. These products include SaaS platforms, CRM systems, and AI-powered solutions, among others. I must emphasize my expertise in designing and maintaining feature stores, as well as creating training/validation sets. Additionally, my deep understanding of building custom neural networks and refining pre-trained models will excel at Amazon Web Services (AWS), which I've leveraged expertise in deploying on SageMaker endpoints. I Clients choose me because they recognize my ability to deliver clean, scalable, and maintainable code alongside robust technical support. I believe our partnership will not only result in the successful completion of your project but also empower your enterprise by automating processes and improving customer experience to drive higher revenue. Let's have a conversation about how my skills and experience can augment your objective to build an exceptional recommendation and ranking pipeline for your enterprise products.

@milanrathod945

Ranking systems fail when feature engineering and model tuning aren't aligned to the actual CTR/CVR metrics. A hybrid approach combining neural ranking with RAG-augmented retrieval is what moves the needle, and I've shipped this on a production 12-agent trading pipeline with structured BUY/SELL/HOLD outputs. For your pipeline, I'd start with feature store design in S3, then build custom neural networks in PyTorch/TensorFlow with retrieval-augmented ranking layers. The same pattern I used on the trading pipeline, where we achieved 18% lift in trade accuracy via hybrid retrieval tuning, applies here. Milestone 1, delivered in under a day: a working PyTorch ranking model prototype on a sample dataset with NDCG metrics. What's the first dataset you'd like me to optimize retrieval against?