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

Senior AI/ML + Distributed Systems Engineer Required (Enterprise Build – $45,000 Fixed Price) Project: AI Financial Intelligence & Risk Monitoring Platform We are hiring a highly experienced engineer or engineering team to architect, build, deploy, and operationalize a production-grade AI Financial Intelligence & Risk Monitoring Platform. This is not a prototype. This is not a simulation. This must be a fully scalable, real-time, enterprise-grade system capable of operating under production conditions. ⸻ Project Overview The platform must: * Process high-volume financial data streams in real time * Detect anomalies using AI-driven models (not rule-based systems) * Generate actionable risk intelligence * Operate under strict performance, latency, and scalability benchmarks Mock pipelines, placeholder logic, or simulated outputs will be treated as non-delivery. ⸻ Budget & Payment Structure – $45,000 Fixed $40,000 – Advance Payment (Testing Phase Funding) * Paid upfront upon project commencement * Intended to fund development, infrastructure, and execution Important: * This payment does not constitute delivery, acceptance, or completion * It does not waive any performance, scope, or deployment requirements * It does not limit the Client’s rights in the event of non-delivery or breach ⸻ $5,000 – Final Payment * Payable only upon: * Full production deployment * All systems operational and accessible * All performance benchmarks achieved * Transfer of: * Source code * Models * Infrastructure access * Documentation Failure to meet these conditions voids entitlement to the final payment. ⸻ Scope of Work (Mandatory Deliverables) Failure to deliver any core component constitutes non-delivery. Data Ingestion & Processing * Distributed ingestion pipeline (Kafka + Spark Streaming) * High-throughput real-time data handling * Data validation and preprocessing pipelines Feature Engineering * Feature store implementation * Real-time and batch feature pipelines AI & Risk Intelligence * Hybrid anomaly detection (statistical + LSTM models) * Ensemble-based risk scoring engine * Real-time inference capability Rule-only systems, hardcoded logic, or simulated outputs do not qualify. ⸻ Explainability Layer * SHAP (or equivalent) * Transparent and interpretable model outputs ⸻ Backend & API * FastAPI-based architecture * Secure APIs (OAuth2 / JWT) * Scalable, modular backend design ⸻ Frontend Dashboard * React-based analytics dashboard * Real-time data visualization * Full integration with backend services ⸻ Infrastructure & Deployment * Kubernetes-based deployment * CI/CD pipelines * Monitoring stack (Prometheus, Grafana) * Staged rollout strategy Local-only or non-scalable deployments do not qualify. ⸻ Testing & Validation * Stress testing under load * Production-readiness verification ⸻ Performance Requirements (Mandatory) The platform must meet all of the following benchmarks: * ≥ 50,000 events/sec ingestion throughput * ≤ 2 seconds ingestion latency * ≤ 50 ms feature retrieval latency * ≥ 90% anomaly detection precision * ≤ 80 ms scoring latency * ≤ 400 ms API response time * ≤ 1.5 seconds dashboard load time Failure to meet these metrics constitutes non-delivery. ⸻ Service Reliability Requirements * Target uptime: 99.5% monthly availability Exclusions include scheduled maintenance, force majeure, external infrastructure failure, and unauthorized usage. Support Expectations Working hours: 09:00–18:00 GMT/BST Response and resolution: * Critical: 4-hour response / 24-hour resolution * Major: 8-hour response / 48-hour resolution * Minor: 1 business day response / 5 business days resolution ⸻ Delivery & Acceptance Standard Delivery is complete only when: * Platform is fully deployed in production * All scope items are fully functional * AI models produce real, verifiable outputs * Performance benchmarks are achieved * Monitoring and CI/CD systems are operational * Documentation is delivered * Full source code, credentials, and infrastructure access are transferred Anything less constitutes non-delivery. ⸻ Data, Security & Compliance The Contractor must implement commercially reasonable safeguards aligned with: * ISO 27001 * SOC 2 (Type I / II) * PCI (where applicable) All work must comply with applicable data protection laws, including GDPR where relevant. The Contractor may use anonymized, aggregated data where legally permitted. ⸻ Intellectual Property Upon final payment: * All deliverables, including source code, models, and documentation, become the property of the Client The Contractor retains ownership of: * Internal tooling * Deployment frameworks * Generic infrastructure components The Client retains full ownership of all data and datasets. ⸻ Confidentiality Both parties agree to maintain strict confidentiality over: * System architecture * Pipelines and models * Data and integrations This obligation survives termination. ⸻ Refund Rights (Material Breach) Failure to deliver a complete, production-ready system constitutes material breach. This includes: * Missed delivery obligations * Non-functional system * Failure to meet performance benchmarks * Failure to deploy to production The Client reserves the right to terminate and pursue recovery of payments subject to applicable terms and governing law. ⸻ Suspension Rights The Contractor may suspend services if: * Payments are overdue beyond 30 days * Agreement breach occurs * Unauthorized usage is detected * Security risks arise ⸻ Limitation of Liability Liability is limited as defined under applicable governing terms. Nothing excludes liability for: * Death or personal injury due to negligence * Fraud * Non-excludable statutory obligations ⸻ Force Majeure Neither party is liable for delays caused by events beyond reasonable control, including: * Natural disasters * War * Infrastructure failures * Pandemics ⸻ Communication Requirement All communication, updates, approvals, and documentation must occur exclusively via Upwork messages. Off-platform communicat