Senior AI/ML + Distributed Systems Engineer Required (Enterprise Build – $45,000 Fixed Price)

@AwaisChaudhry

Your specification calls for a real production platform, not a dashboard wrapped around mock data. I would shape the delivery around measurable gates: Kafka and Spark streaming for partitioned ingestion, validation, feature pipelines, and load-tested throughput; hybrid anomaly detection and ensemble risk scoring for verifiable Machine Learning (ML) outputs; and explainability through SHAP. The service layer would expose secure, modular API Development through FastAPI with OAuth2/JWT, while a React.js dashboard consumes real-time metrics and risk signals efficiently. Kubernetes, CI/CD, Prometheus, and Grafana would support staged deployment, observability, rollback, and operational readiness. Stress tests should validate each stated latency, throughput, precision, availability, and dashboard target before production acceptance. I would begin with a benchmark-driven architecture review, data-contract definition, and capacity model, then deliver ingestion, features, models, APIs, frontend, deployment, and validation as one traceable production path. A realistic enterprise implementation should be planned after confirming data sources, integrations, and acceptance test environments; the initial estimate would typically be phased across architecture, build, hardening, and production rollout rather than guessed from the headline scope. Which real-time data sources and representative event schemas will define the 50,000-events/sec benchmark and model-validation ground truth? Muh

@sajjadtaghvaeifr

Hello, I trust you're doing well. I am well experienced in machine learning algorithms, with nearly a decade of hands-on practice. My expertise lies in developing various artificial intelligence algorithms, including the one you require, using Python, and similar tools. I have worked with pytorch, and tensorflow to develop DL models, .I hold a doctorate from Tohoku University and have a number of publications in the same subject. My portfolio, which showcases my past work, is available for your review. Your project piqued my interest, and I would be delighted to be part of it. Let's connect to discuss in detail. Warm regards. please check my portfolio link: https://www.freelancer.com/u/sajjadtaghvaeifr

@sufyanasmat38

Hello, I have strong experience with AI/ML, Kafka, Spark, FastAPI, Kubernetes, real time data pipelines, anomaly detection, and scalable distributed systems. I can build the production platform across ingestion, feature engineering, LSTM and statistical anomaly detection, risk scoring, SHAP explainability, secure APIs, React dashboards, CI/CD, monitoring, and Kubernetes deployment. I’ll focus on the required throughput, latency, reliability, testing, and production validation rather than mock or simulated outputs. I can provide the architecture, team structure, timeline, and relevant production work and would be happy to discuss the data sources and deployment environment.

@intelliresponse

Hello, I have experience designing and building production-grade AI and distributed systems, including real-time data pipelines, machine learning inference services, and scalable cloud architectures. I can help architect a reliable platform using Kafka, Spark Streaming, Kubernetes, FastAPI, and React with a focus on performance, security, and maintainability. My proposed approach would include a scalable event ingestion layer with Kafka, stream processing using Spark, feature engineering pipelines, and an ML risk engine combining statistical methods with deep learning models such as LSTM-based anomaly detection. I will implement explainable AI using SHAP or equivalent techniques so risk decisions remain transparent and auditable. The backend will be designed with modular FastAPI services, secure authentication, asynchronous processing, and production-ready APIs. The frontend dashboard will provide real-time analytics and monitoring, while Kubernetes, CI/CD pipelines, Prometheus, and Grafana will ensure reliable deployment and operational visibility. I will provide complete source code, infrastructure configuration, documentation, and deployment support. Before development, I would finalize the architecture, define performance testing methods, and establish measurable benchmarks to ensure the final system meets the required production standards.

@ZohaibRoy

⭐⭐⭐⭐⭐ Build a Scalable AI Financial Intelligence & Risk Monitoring Platform ❇️ Hi My Friend, I hope you are doing well. I've reviewed your project requirements and see you are looking for a Senior AI/ML and Distributed Systems Engineer. You don’t need to look any further; Zohaib is here to help you! My team has successfully completed 50+ similar projects for AI-driven platforms. I will create a robust financial intelligence system, ensuring it meets performance and scalability needs. ➡️ Why Me? I can easily build your AI Financial Intelligence & Risk Monitoring Platform as I have 5 years of experience in AI, machine learning, and distributed systems. My expertise includes real-time data processing, anomaly detection, and feature engineering. Additionally, I have a strong grip on technologies like Kafka, Spark, Kubernetes, and FastAPI, ensuring a comprehensive solution. ➡️ Let's have a quick chat to discuss your project in detail. I can show you samples of my previous work that reflect my capabilities. Looking forward to our conversation! ➡️ Skills & Experience: ✅ AI Development ✅ Machine Learning ✅ Data Engineering ✅ Real-time Processing ✅ Anomaly Detection ✅ Feature Engineering ✅ Kafka ✅ Spark ✅ Kubernetes ✅ FastAPI ✅ React ✅ CI/CD Pipelines Waiting for your response! Best Regards, Zohaib

@emilesd617

Hi, I am a senior AI/ML and distributed systems developer with 8 years of rich experience in software development, with a strong background in real-time data platforms, machine learning, scalable backend systems, and cloud infrastructure. I am familiar with Python, Kafka, Spark Streaming, FastAPI, React, Kubernetes, Docker, PostgreSQL, Redis, XGBoost, PyTorch, LSTM, SHAP, Prometheus, Grafana, OAuth2, JWT, CI/CD, and distributed system design. For your platform, I can build the ingestion, feature, anomaly-detection, scoring, explainability, API, dashboard, monitoring, and Kubernetes deployment layers as one production system, with load testing and measurable performance validation rather than simulated outputs. I'm an individual freelancer and can work on any time zone you want. Please contact me with the best time for you to have a quick chat. Thanks. Emile.

@gotucat

Hi, I reviewed the project and understand the main requirement: a production-grade AI Financial Intelligence and Risk Monitoring Platform, not a prototype, with real-time scalability and verifiable performance. I can structure the implementation around distributed ingestion with Kafka and Spark Streaming, feature engineering, AI-driven anomaly detection, LSTM models, ensemble risk scoring, SHAP explainability, and real-time inference. The backend will use FastAPI with secure APIs, while Kubernetes, CI/CD, Prometheus, Grafana, and stress testing support production deployment and validation against the stated benchmarks. React dashboard integration will complete the operational workflow. I’ll maintain clean, documented, responsive delivery with careful attention to security, observability, latency, and reliability requirements. Let’s discuss here now.

@HamparProd

The architecture is feasible, but I would only commit to the production benchmarks once the test dataset, infrastructure profile, payload sizes, concurrency and benchmark methodology are defined. I would use Kafka + Spark Structured Streaming for ingestion, separate batch/realtime feature pipelines, a versioned feature store, statistical baselines plus LSTM/ensemble anomaly models, SHAP-based explainability, and FastAPI services for scoring and platform APIs. React would consume aggregated realtime metrics, with Kubernetes, Prometheus/Grafana and CI/CD supporting production operations. The first technical milestone must establish representative-data benchmarks and freeze the acceptance methodology. Performance targets such as 50k events/sec and 90% anomaly precision cannot be responsibly guaranteed without defined ground truth and load conditions. I would also require milestone-level acceptance/payment rather than the current strict no-partial-acceptance structure. I can begin once the commercial terms and benchmark definitions are corrected.

@mayr81

I’m ready to architect and deliver a production-grade AI Financial Intelligence & Risk Monitoring Platform that meets the stated performance, reliability, and security requirements. Approach (enterprise build, not prototypes): - Distributed real-time ingestion using Kafka + Spark Streaming with validation/preprocessing. - Feature engineering with a proper real-time/batch feature store. - Hybrid, AI-driven anomaly detection (LSTM + statistical methods) with an ensemble risk scoring engine and real-time inference. - Explainability via SHAP (or equivalent) integrated into the scoring workflow. - FastAPI backend with OAuth2/JWT secure APIs and modular services supporting strict latency targets. - React analytics dashboard with real-time visualization wired end-to-end. - Kubernetes deployment with CI/CD, staged rollout, and full observability (Prometheus/Grafana). - Stress/load testing to verify throughput (≥50k events/sec), latency (≤2s ingest, ≤50ms feature retrieval, ≤80ms scoring, ≤400ms API), and API/UI performance. - Production-readiness checks, documentation, and full operational transfer (source code, models, infra access). Deliverables and acceptance are aligned with your “no partial acceptance” requirement.

@AndrewJSMAN

I've built production-grade real-time financial risk systems using Kafka, Spark, and Kubernetes, so this is right in my wheelhouse. I'll set up a Kafka-Spark Streaming pipeline for ingestion, deploy a feature store with real-time/batch pipelines, and build a hybrid anomaly detection system with LSTM and statistical models. The FastAPI backend will use OAuth2/JWT, and the React dashboard will integrate with Prometheus/Grafana for monitoring. I'll stress-test the system to meet all latency and throughput benchmarks before deployment. I can start immediately. Thanks, Andrii.

@CorporateRD

Hi there, This is the kind of AI platform where architecture, throughput and production reliability matter as much as the ML models themselves. Royal Designs has the engineering experience to build scalable AI and distributed backend systems for real production environments. For this build, we would approach it as a production system from day one, using Kafka and Spark Streaming for high volume ingestion, real time and batch feature pipelines, statistical plus LSTM anomaly detection, ensemble risk scoring, SHAP explainability, FastAPI services and a React analytics dashboard. Kubernetes, CI/CD, Prometheus and Grafana would be integrated into the architecture, with proper stress testing against your 50,000+ events/sec throughput, latency and API performance requirements. Our team can handle AI/ML, backend, frontend, DevOps and QA under one delivery process. The $45,000 fixed budget and production acceptance requirements are understood. We can provide the architecture, team structure and timeline after reviewing the available infrastructure and data. Let’s have a quick chat or call to discuss the project and finalize the technical approach. Regards, Royal Designs (FK)

@snbhanja

Hi, I've spent the last 11+ years building real-time fraud and anomaly detection prod platforms on Kafka + Spark Streaming sustaining 50k+ events/sec, with LSTM and autoencoder models (univariate and multivariate, including online/real-time detection) hitting 90%+ precision on live financial data. My usual architecture for this: a feature store on Redis for sub-10ms online lookups, an ensemble scoring engine (statistical + deep models) served via FastAPI, SHAP explanation on every score, all running on Kubernetes with CI/CD, Prometheus/Grafana monitoring, and a React dashboard on top. I've shipped and operated these end-to-end. Most recently a card fraud and anomaly detection platform for a payments processor, which ran at 55,000+ events/sec in production with 93% precision, and before that a multivariate anomaly detection system for a fintech where I built the full pipeline from Kafka ingestion through Spark feature computation to alerting. Your benchmark numbers are realistic with this stack, and I'd validate every one of them with k6/Locust load tests before handover, not after. Lets connect. I can send you a one-page architecture diagram and a phased delivery plan (~16–20 weeks to production) on a call this week.

@juanpablomacf

⭐⭐⭐⭐⭐ Built real‑time AI risk platforms with Kafka+Spark pipelines, anomaly models, FastAPI, React and full Kubernetes scaling.

@longngoc2307

Hi, there. "AI FINANCIAL INTELLIGENCE PLATFORM" — you need a production system that can process risk signals at scale I would approach this as a distributed architecture problem first: streaming ingestion with Kafka, processing pipelines with Spark, model serving through FastAPI, and Kubernetes-based deployment with monitoring from the start. The AI layer would focus on real inference workflows, combining statistical methods with ML models, feature pipelines, explainability, and measurable validation instead of placeholder outputs. For a system with strict latency and throughput targets, the difficult parts are usually the connections between components: feature freshness, model response time, scaling behavior, and recovery during load spikes. I would define service boundaries, data flow, and benchmark tests early so every stage can be measured against the required targets. I would deliver through clear milestones covering architecture, ingestion and processing, AI inference, APIs, deployment, and production verification. Which part is already available today: financial data sources, existing ML models, or the infrastructure environment for deployment? Looking forward to working with you.

@ianb65

Hi there, Real-time financial risk monitoring at 50,000+ events/sec needs a production FastAPI backend, not a prototype, with low-latency Data Processing and reliable Cloud Computing deployment. I have strong hands-on experience with API Development and Machine Learning (ML) systems and have solved similar problems involving streaming ingestion and production monitoring. My approach would be to: * Review the existing code, architecture, logs, or current implementation. * Identify the root cause and determine what can be reused versus what needs to be changed. * Implement a clean, maintainable solution using FastAPI, Cloud Computing, Data Processing, and Kubernetes. * Test edge cases, reliability, performance, security, and integration behavior. * Deploy or integrate the fix without disrupting the existing system. A key technical focus will be keeping ingestion, feature retrieval, and scoring paths independently scalable so latency stays predictable under load. I prefer fixing the actual root cause rather than applying temporary patches, and I will keep the implementation simple, stable, and maintainable. I can start by reviewing the current setup and proceed with the most practical solution. Best regards, Ian

@Ahmedyar547005

As an experienced data engineer and AI/ML architect, I am confident in my ability to build a robust and scalable financial intelligence and risk monitoring platform that meets your stringent requirements.I have a solid understanding of both AI-driven models and distributed systems, which are essential for creating a high-performing system like the one you're looking to develop. My proficiency with technologies such as Kafka, Spark Streaming, Kubernetes, and FastAPI aligns seamlessly with your infrastructure's needs. Moreover, my problem-solving skills have always allowed me to come up with unique solutions even in complex situations. This ensures that I am always focused on finding the best way to deliver real-time data processing, distributed ingestion pipelines, real-time and batch data handling, and more – all exactly per your project description. I’m committed to delivering the highest quality results within deadlines – as reflected by my track record of successful project completions. By choosing me for this critical project, you can be assured of not just attentiveness and expertise but also transparency and effective communication throughout the process. Let’s discuss how we can exceed your project expectations!

@murtaza1122

Hi, I hope you are doing well. I have recently developed similar system which takes care of internal it audit. Though throughput is not similar as we are using large language model and RAG enabled system. System actually take care of high risk areas, identify them and then prepare a plan for audit based on risk and frequency metrices. I can share a demo for the system and we can discuss financial risk monitoring platform. I have deployed AI models on edge devices for within 1 sec throughput but that was for vision data with around 40 frames, this experience will enable me to reach your target as I practiced real time and quantized ML in my previous projects. Other than that been working on cloud docker, kubernetes and kafka for data streams along CI/CD pipelines. Lets discuss

@technologiesit

Having successfully delivered over 200 projects, I am a seasoned full-stack developer & AI solutions expert that guarantees high-quality, scalable, and pragmatic solutions. I’m well-versed in the technologies you demand: Kafka + Spark Streaming for data ingestion, FastAPI for backend management, and React for frontend dashboard analytics. With my knowledge of Kubernetes-based deployment and CI/CD pipelines, I can ensure fast-paced, reliable and scalable production of your enterprise-level AI Financial Intelligence & Risk Monitoring Platform. Importantly, I understand that this is not just about coding an application but also ensuring reliability, support, and ongoing performance. Professionally endorsing the ’95% On-Time’ and ’98% On-Budget’ delivery record I bring to bear. Let's connect to discuss your project further - trust me to build not just a system but a robust solution that meets all your requirements.

@revival786

Greetings! I can architect and build your AI Financial Intelligence and Risk Monitoring Platform as a true production-grade system. My approach would center on a Kafka and Spark Streaming ingestion layer, a feature store supporting both real-time and batch pipelines, and hybrid anomaly detection combining statistical methods with LSTM models for ensemble risk scoring. I would add a SHAP-based explainability layer, a FastAPI backend with OAuth2 and JWT security, and a React dashboard with live visualization. Deployment would run on Kubernetes with CI/CD, Prometheus and Grafana monitoring, and a staged rollout. I would build toward your performance benchmarks from day one, including stress testing and production-readiness validation. I understand the payment structure, the strict acceptance terms, and the no partial acceptance clause. Happy to discuss architecture and timeline further. Thanks, Revival