Hire the Best AWS Lambda Developers

AI Agent Architect | Claude Code | AWS Bedrock | LangGraph | n8n | RAG | Full Stack AI Systems I build production-grade AI systems, autonomous agents, and enterprise SaaS platforms that integrate deeply with real business operations. Over the last 11+ years, I've worked as a Software Engineer, Team Lead, CTO, and AI Architect, helping startups and enterprises design, build, deploy, and scale cloud-native products across AI, FinTech, Analytics, Media, eCommerce, and Enterprise SaaS. My focus today is building AI-native systems that move beyond simple chatbot experiences and become reliable business operators capable of reasoning, planning, executing tasks, and integrating with enterprise workflows. ━━━━━━━━━━━━━━━━━━━━━━ AI Agents & Agentic Systems I design and build production-ready AI agents capable of: • Autonomous decision making • Multi-step reasoning and execution • Tool calling and API orchestration • Multi-agent collaboration • Planner → Supervisor → Worker architectures • Long-term memory and contextual learning • Human-in-the-loop workflows • Event-driven automation systems • Enterprise knowledge retrieval • Agent evaluation and reliability testing Technologies: • Claude API (Anthropic) • OpenAI GPT-4o / Realtime API • AWS Bedrock • LangGraph • LangChain • MCP (Model Context Protocol) • CrewAI • Vector Databases • Knowledge Graphs Recent implementations include: • Financial Intelligence Agents • Company Research Agents • B2B Outreach Agents • AI Website Builders • AI Content Generation Systems • Customer Support Agents • Voice AI Agents • Enterprise Document Intelligence Platforms ━━━━━━━━━━━━━━━━━━━━━━ Retrieval-Augmented Generation (RAG) I build enterprise-grade RAG architectures designed for accuracy, traceability, and scalability. Capabilities: • Enterprise document ingestion pipelines • PDF, DOCX, HTML, Database ingestion • Chunking and embedding optimization • Hybrid retrieval architectures • Metadata filtering • Re-ranking pipelines • Knowledge graph integration • Hallucination reduction • Citation and provenance systems • Multi-tenant knowledge bases Vector Technologies: • Pinecone • OpenSearch • ElasticSearch • pgvector • Weaviate • FAISS • Supabase Vector Recent projects include enterprise document intelligence platforms and governed reasoning systems for business-critical workflows. ━━━━━━━━━━━━━━━━━━━━━━ n8n & Workflow Automation I build advanced automation systems using n8n that combine deterministic workflows with LLM reasoning. Capabilities: • AI-powered workflow orchestration • Agent-driven automation pipelines • Human approval workflows • Event-driven architecture • Queue-based processing • Retry-safe workflows • Background job processing • Cross-platform integrations • Multi-step decision engines Integrations: • Slack • Google Workspace • Microsoft 365 • HubSpot • Salesforce • Stripe • Twilio • Airtable • Notion • Internal APIs ━━━━━━━━━━━━━━━━━━━━━━ Voice AI & Conversational Systems Design and implementation of production-ready voice agents using: • Twilio Voice • OpenAI Realtime API • Claude-powered reasoning • AWS Transcribe • Whisper • ElevenLabs • Stateful conversation engines • Automated call summaries • CRM integrations Use Cases: • Appointment Booking • Lead Qualification • Customer Support • Sales Outreach • Reservation Systems • Internal Operations ━━━━━━━━━━━━━━━━━━━━━━ AWS Cloud & AI Infrastructure Strong background designing cloud-native AI platforms on AWS. Core Services: • AWS Bedrock • Lambda • ECS • EC2 • API Gateway • SQS • SNS • EventBridge • DynamoDB • Aurora • PostgreSQL • S3 • CloudFront • CloudWatch • Step Functions Infrastructure: • Terraform • CloudFormation • Docker • Kubernetes • CI/CD Pipelines • Multi-Tenant SaaS Architectures ━━━━━━━━━━━━━━━━━━━━━━ Full Stack AI Product Development Frontend: • React • Next.js • TypeScript Backend: • Node.js • Express • NestJS • Python • FastAPI Databases: • PostgreSQL • MySQL • MongoDB • DynamoDB • Redis ━━━━━━━━━━━━━━━━━━━━━━ Industries Served • FinTech • Enterprise SaaS • LegalTech • Healthcare • Media & Broadcasting • Crypto & Blockchain • Analytics Platforms • Marketing Automation • Cloud Infrastructure ━━━━━━━━━━━━━━━━━━━━━━ What Clients Typically Hire Me For ✓ Multi-Agent Systems ✓ Claude & AWS Bedrock Development ✓ Enterprise RAG Platforms ✓ n8n AI Automation ✓ AI SaaS Products ✓ Voice AI Agents ✓ Workflow Orchestration ✓ AI Architecture Reviews ✓ AWS Cloud Architecture ✓ Production Launches & Scaling If you're building an AI product and need someone who understands both agentic AI and production-grade software architecture, I can help design, build, deploy, and scale the entire system. ✓ Claude Code & OpenAI GPT-4o ✓ Multi-Agent Systems ✓ LangGraph & RAG Pipelines ✓ n8n & Workflow Automation ✓ Voice AI (Twilio, ElevenLabs) ✓ Node.js, React & AWS Bedrock ✓ Stripe, SaaS & Enterprise Integrations ✓ AI Agents for Finance, Legal, Healthcare & Op

👋 Hi, I’m Aziza, a Top Rated Plus Python Backend Engineer with 8 years of experience building backend systems, APIs, automation tools, data pipelines, and production-ready applications. I help businesses replace messy, manual, or outdated backend workflows with clean, reliable software that is easier to use, easier to maintain, and ready to scale. If you need someone who can step into an existing codebase, understand the business problem quickly, ask the right questions, and deliver without heavy supervision, I can help. 🚀 What I do best: • Build Python backend systems from scratch • Create and improve Django, DRF, FastAPI, Flask, and Django Ninja APIs • Convert Bash scripts and legacy workflows into modern Python applications • Build internal dashboards, admin tools, and automation platforms • Design web scraping and data extraction pipelines • Integrate third-party APIs, payments, authentication, and cloud services • Improve database structure, queries, performance, and reliability • Add Celery jobs, scheduled tasks, logging, and background processing • Debug production issues and clean up existing backend systems • Use Claude, Cursor, and AI tools to move faster while keeping code clean and production-ready 🛠️ Technologies I work with: ✅ Python ✅ Django ✅ Django REST Framework ✅ Django Ninja ✅ FastAPI ✅ Flask ✅ PostgreSQL ✅ MySQL ✅ MariaDB ✅ MongoDB ✅ Supabase ✅ Celery ✅ Redis ✅ AWS S3 ✅ AWS Lambda ✅ Railway ✅ Cloudflare ✅ Docker ✅ GitHub Actions ✅ Bitbucket ✅ Stripe API ✅ RevenueCat ✅ Firebase ✅ OneSignal ✅ Swagger / OpenAPI ✅ Pytest ✅ BeautifulSoup ✅ Selenium ✅ Claude ✅ Cursor 💼 Recent results: • Modernized 30+ fragmented Bash scripts into a clean Django web application • Recreated complex legacy logic in Python with better structure, logging, error handling, and documentation • Worked with PostgreSQL and MariaDB inside one Django project • Consolidated multiple API environments into a single Django REST Framework application • Built backend APIs and data workflows for a data-heavy analytics dashboard • Worked with scraping, onboarding flows, Supabase, Railway, Clerk, and Cloudflare • Delivered backend work that clients described as clean, efficient, well-structured, and easy to maintain 🤖 AI-assisted development: I actively use Claude, Cursor, and AI coding tools for research, planning, debugging, prompt work, refactoring, and implementation. I use AI to speed up development, but I still review, test, and structure the code carefully so the final result is reliable and maintainable. ✨ Why clients choose me: Clients describe me as a clear communicator, reliable, proactive, collaborative, detail-oriented, and committed to quality. I care about the real business goal behind the code. I do not just “finish tickets.” I help improve the system, reduce manual work, make workflows smoother, and leave behind code that other developers can understand and maintain. If your backend needs to be built, fixed, automated, modernized, or scaled, I’d be happy to learn more about your project. 🚀

I build infrastructure that stays up when it matters. Government platforms for 2M+ citizens, banking systems for 1M+ users, and AI rendering stacks on GPU Kubernetes clusters. Shipped on time, documented for the team that inherits it. 5+ years owning the full stack: physical hardware, hypervisors, Kubernetes, service mesh, cloud networking, CI/CD, observability, and GPU AI workloads. Across government, fintech, SaaS, and deep-tech startups. Government Scale — OpenG2P Ethiopia, 3 Ministries Leading platform engineering for Ethiopia's national OpenG2P welfare system across ATI, MoWSA, and EDRMC. Serving 2M+ beneficiaries on RKE2 with Rancher, Istio mTLS, Keycloak SSO, WireGuard site-to-site VPN, XCP-ng hypervisor with Hardware RAID 1+0, and a full Prometheus + Grafana + Alertmanager + Loki observability stack. Currently executing a large-scale ODK to OpenG2P data migration with schema mapping, validation pipelines, audit trails, and phased rollback. AWS Serverless Architecture — WeTruck Architect and operator of WeTruck's serverless logistics platform. CloudFront to ALB to WAF (deny-by-default with Telebirr payment gateway IP whitelisting) to Lambda (FastAPI via Mangum). Fully Terraformed infrastructure. GitHub Actions with OIDC federation (zero long-lived credentials), RDS PostgreSQL, DynamoDB for JWT token store, SQS with dead-letter queue, Secrets Manager, AWS Amplify for three Next.js frontends (back-office, transporter, shipper), and Route 53 private/public DNS. I own production incidents end-to-end and authored the full deployment runbook and troubleshooting playbook. AWS in daily use: Lambda, EC2, EKS, CloudFront, ALB, WAF, RDS, DynamoDB, SQS, Secrets Manager, Amplify, ECR, Route 53, IAM OIDC, VPC (private subnets, NAT Gateway, VPC endpoints), CloudWatch, ACM. MPLS and AI Rendering Infrastructure — MetaPlux (Tech Lead) Tech Lead on a distributed GPU-aware AI rendering platform. MPLS networks for deterministic, low-latency routing across distributed rendering nodes with label-switched paths and traffic engineering for QoS-sensitive GPU workload traffic. BGP and OSPF routing across hybrid environments connecting on-premises GPU clusters to cloud egress points. WireGuard VPN tunnels for secure, auditable connectivity. Multi-cloud strategy across AWS and Azure for rendering workload distribution. Platform architecture for GPU-aware Kubernetes scheduling and job orchestration. This role sits at the intersection of deep networking and modern cloud infrastructure, the combination most DevOps engineers don't have. AI and GPU Infrastructure — Exponent.ch (Switzerland) Provisioned and managed NVIDIA GPU-enabled RKE2 clusters running AI and automation workloads: n8n, Langfuse LLM observability, LibreChat, and ClickHouse analytics. Standardized Helm deployments across 5+ environments using shared-stacks architecture. Integrated Teleport for zero-trust cluster access, automated secrets via Lade and 1Password. Prometheus + Grafana + Mimir observability with 99.9% metric coverage, 60% MTTR reduction, and 3 critical outages prevented through proactive alerting. Banking Infrastructure — 6 Ethiopian Banks, 1M+ Users Delivered high-availability mobile banking and USSD platforms for Siinqee Bank, Hijra Bank, Wegagen Bank, and others with 99.95% uptime across 1M+ users. Designed MPLS network architecture for multi-branch WAN connectivity with QoS for transaction data. Built Jenkins CI/CD for Maven Java apps (4 hours to 15 minutes). ELK processing 10TB+ daily logs. Proxmox and AWS hybrid cloud with ~35% cost reduction. Full Technical Stack Container Orchestration: Kubernetes (RKE2, EKS, K3s), Docker, Helm, Rancher, OpenShift Infrastructure as Code: Terraform, Terragrunt, Ansible, OpenTofu CI/CD: GitHub Actions, GitLab CI, Jenkins Cloud: AWS (full suite), Azure, OpenStack Networking: MPLS, BGP, OSPF, WireGuard, HAProxy, NGINX, VLAN, VPC design Service Mesh: Istio (mTLS, traffic policies, ingress) Observability: Prometheus, Grafana, Mimir, Alertmanager, Loki, OpenTelemetry, ELK, Zabbix, SigNoz Security and Identity: Keycloak, HashiCorp Vault, Teleport, IAM OIDC Virtualization: Proxmox, XCP-ng, VMware AI and Automation: NVIDIA GPU Kubernetes, YOLO, ONNX, TensorRT, n8n, Langfuse, LibreChat, ClickHouse Databases: PostgreSQL, MySQL, MongoDB, Redis, Elasticsearch, DynamoDB Backend: Python (FastAPI), Bash, Node.js How I work I overcommunicate early, give realistic timelines, and ship verified, not "it should work." I treat testing as the real deliverable, not a checkbox. If something is out of scope or a bad idea, I'll tell you before you spend money on it. Message me if you need: - AWS or Kubernetes infrastructure built or fixed - CI/CD pipelines developers actually trust - GPU and AI infrastructure with inference pipelines and observability - Migration from cloud to cloud, or on-prem to production cloud - MPLS, BGP, or complex network architecture across hybrid environments - Observability that catches problems before users do

🏆 TOP RATED PLUS Data Expert | Top 3% on Upwork 💰 $600K+ earned | 16,000+ hours | 130+ clients served I am a Sr Data Engineer with expertise in developing robust AI Agents & Analytics layer. I bring over 8 years of hands-on experience with: - Building scalable ETL data pipelines that fetch raw data froms APIs and store it into data warehouses hosted over GCP/AWS (BigQuery | Snowflake | Redshift). - Developing Business Intelligence reports and dashboards using Data Studio (formerly Looker Studio), Metabase, Looker, Tableau etc. - Building powerful AI Agents using Gemini, Claude, OpenAI, Dialogflow. - Track user behaviour data using GA4 and Google Tag Manager. I’ve worked with 130+ clients across eCommerce (Shopify, WooCommerce), digital marketing & paid ads (Meta, Google Ads), mobile apps & gaming analytics, SaaS & web apps, and data-driven businesses in education, clean energy & media. 💡 What I do (End-to-End Ownership) 1. Data Engineering & Warehousing: - Build scalable, reliable data pipelines using BigQuery, Snowflake, Redshift, Python and APIs of data sources - Automated ETL (Airflow, APIs, Fivetran, custom Python scripts) - Single source of truth across marketing + product + revenue 2. Analytics & BI (Decision Systems, not just dashboards): - Executive dashboards (Looker, Data Studio (formerly Looker Studio), Metabase, Power BI, Tableau) - KPI frameworks aligned to revenue - Cohort, LTV, attribution & funnel analysis 3. Marketing & Web Tracking (Accuracy = $$$) - GA4, Google Tag Manager, Server-side tracking - Meta CAPI, Google Ads, TikTok tracking - Fix broken attribution & data loss 4. Generative AI & Automation - AI agents & workflows (OpenAI, Gemini, Claude) - Automate reporting, insights, and ops - Use AI where it actually improves ROI (not hype) 📈 Real Outcomes I’ve Delivered ✔ Built full marketing data warehouse → improved spend efficiency by 30%+ ✔ Fixed tracking & attribution → recovered lost revenue visibility ✔ Automated reporting → saved 20+ hrs/week for teams ✔ Delivered exec dashboards → faster, data-backed decisions 🧠 Why Clients Choose Me - I think like a business owner, not just an engineer - I focus on revenue impact, not vanity metrics - I handle end-to-end (tracking → pipelines → dashboards → insights) - Strong communication + fast execution (no hand-holding needed) 📈 My Tech Stack: - Business Intelligence & Data Visualisation: Google Data Studio (formerly Looker Studio) , Looker, Metabase, Power BI, Mode, Tableau, Databox, Zoho Analytics, DOMO, Google Sheets, etc. - AI: LLMs like OpenAI, ChatGPT, Gemini, Claude, DeepSeek, and GCP's services like Document AI, DialogFlow, CCAI, etc. - Engineering: SQL, Python, Airflow, APIs, Cloud Functions, Lambda Functions, Cloud Composer, Cloud Run - Data Warehouses: BigQuery, Redshift, MS SQL, MySQL, PostgreSQL, Snowflake, and Azure. - ETL & Webhook tools: n8n, Fivetran, Stitch, Windsor, Supermetrics, Power My Analytics, Saras Analytics, Zapier, Make, etc. - Tracking: Google Tag Manager, Google Analytics 4, Meta Ads Conversion API, Google Ads Conversion tracking, Stape, Server-side tracking. - Data Sources: Shopify, WooCommerce, BigCommerce, Meta Ads, Google Ads, TikTok Ads, Pinterest Ads, LinkedIn Ads, Apple Ads, Amazon Ads, Bing Ads, Google Analytics 4, Google Search Console, Google My Business, HubSpot, Active Campaign, PipeDrive, Facebook Page Insights, Instagram Insights, Stripe, SEMRush, MailChimp, Klaviyo, ClickUp, Ahref, etc. 👉 Let’s Work If you’re looking for someone who can own your entire data stack and turn it into a revenue engine, let’s talk. Click “Invite” and let’s discuss your use case 🚀 ----- 🔍 𝗞𝗘𝗬𝗪𝗢𝗥𝗗𝗦 GA4, Google Analytics 4, Google Tag Manager (GTM), Server-side Tracking, Meta Conversion API (CAPI), Google Ads Conversion Tracking, Marketing Attribution, BigQuery, Snowflake, Redshift, Data Warehouse, Big Data, Data Engineering, ETL, ELT, Data Pipelines, Apache Airflow, Airflow DAGs, PySpark, Spark, Databricks, SQL, Python, Advanced SQL, Data Modeling, Data Transformation, Data Architecture, Data Lakes, Data Studio, Looker Studio, Power BI, Tableau, Data Visualization, Dashboard Development, Business Intelligence (BI), KPI Dashboard, Reporting Automation, Shopify Analytics, WooCommerce Analytics, Marketing Analytics, Product Analytics, Funnel Analysis, Cohort Analysis, LTV Analysis, Retention Analysis, Generative AI, OpenAI, ChatGPT, Gemini, Claude, AI Agents, AI Automation, AI Agents, LLM Applications, n8n, Workflow Automation, No-code Automation, Low-code Automation, Zapier, Make (Integromat), API Integrations, Webhooks, Stripe, HubSpot, Google Ads, Meta Ads, TikTok Ads, LinkedIn Ads, Cloud Platforms (GCP, AWS), Cloud Functions, AWS Lambda, Data Orchestration

If your AWS bill keeps climbing, production breaks under load, or a compliance deadline is getting close - the problem is rarely just AWS. It’s the architecture. I step into fragile systems and fix both: cloud infrastructure, and the application design running on it. Because even perfect AWS setup cannot save software that wasn’t designed to scale, isolate failures, or handle real production traffic. Most teams I work with are stuck in one of these situations: - AWS costs rising every month with no clear reason - Systems that pass tests but fail under real traffic - Compliance pressure from regulators or auditors - Constant firefighting instead of controlled releases Hiring more engineers or adding more monitoring increases complexity. I simplify the architecture - both infrastructure and code-level design - so the problems stop repeating. Recent outcomes: - Avoided a $5M federal penalty by delivering a critical federal platform before deadline, focusing only on what was required for compliant launch - Cut hosting costs from $96K to $14K per year by eliminating architectural waste across infrastructure and services - Designed and delivered a FedNow-ready, Federal Reserve-compliant real-time payments system scaling to 60,000 transactions per second using horizontally scaled services built specifically for cloud behavior What changes after I’m done: - Infrastructure and software designed to scale together - Predictable AWS costs - Systems that stay stable under load - Clear documentation and runbooks - A team that can operate it independently I build boring, predictable systems on purpose. Boring systems survive audits, scale events, and 3am incidents. If you’re dealing with cost pressure, reliability issues, or compliance risk, send me your biggest concern. I’ll tell you exactly what I would fix first and whether it’s worth doing. If it’s not a fit, I’ll say so.

Building an AI agent or RAG demo is easy. Making it reliable, secure, observable, and financially predictable in production is where most projects fail. I help SaaS and enterprise teams design, build, and rescue production AI systems on AWS. Expert-Vetted Top 1% AI Consultant on Upwork - 100% Job Success - $80K+ earned - 2,700+ hours delivered - 41 Upwork engagements - 9+ years of production engineering experience - Three AWS Professional certifications in Generative AI, Solutions Architecture, and DevOps ✓ CLIENTS USUALLY CONTACT ME WHEN - An AI agent works in a demo but fails with real users, tools, permissions, or business data - A RAG system retrieves the wrong information, produces weak citations, or becomes unreliable at scale - An AI proof of concept needs to become a secure and maintainable production product - Claude, OpenAI, or Amazon Bedrock must be connected to real business workflows - AWS infrastructure has reliability, security, scalability, or cost problems - A development team needs senior technical ownership rather than another task-based developer - An existing implementation needs architecture review, debugging, or production rescue → WHAT I BUILD PRODUCTION AI AGENTS - AI agents and agentic workflows - Claude, OpenAI, and Amazon Bedrock integrations - Tool calling, MCP servers, and external system integrations - Human approval and escalation workflows - Agent memory, state management, and context handling - Evaluations, guardrails, tracing, and failure recovery - Multi-agent orchestration where it is genuinely justified PRODUCTION RAG SYSTEMS - Document ingestion and processing pipelines - Vector search and hybrid retrieval - Metadata filtering, reranking, and access control - Reliable citations and source attribution - Retrieval and response evaluations - Enterprise knowledge assistants - Multi-tenant RAG architecture - OpenSearch and vector database integrations AWS ARCHITECTURE AND PLATFORM ENGINEERING - Serverless and event-driven systems - AWS Lambda, API Gateway, DynamoDB, SQS, EventBridge, and Step Functions - Terraform, AWS CDK, CloudFormation, and infrastructure automation - IAM, tenant isolation, security boundaries, and least-privilege access - Monitoring, observability, incident recovery, and architecture reviews - AWS cost optimization, performance improvement, and cloud migrations ★ SELECTED PRODUCTION EXPERIENCE - Worked on a high-volume ordering platform processing millions of transactions per month with strict uptime, performance, and recovery requirements - Delivered a 686-hour AWS engagement involving Cognito, Lambda, SQS, backend services, and production infrastructure, followed by a five-star client review - Improved production systems across SaaS, healthcare, fintech, cybersecurity, and streaming - Owned delivery across architecture, critical implementation, deployment, security, observability, and engineering handoff I work hands-on across Python, Node.js, TypeScript, APIs, distributed systems, cloud infrastructure, and modern AI frameworks such as LangGraph and LangChain. → HOW I WORK I do not begin by selecting an AI framework. I begin with: - The business workflow - The users and permission boundaries - The available data - The expected result - The failure modes - The security requirements - The operating cost - The team's ability to maintain the system From there, I design the smallest architecture that can safely achieve the required outcome. I will also tell you when: - An AI agent should be a deterministic workflow - RAG is unnecessary for the use case - A managed service is creating avoidable cost or lock-in - The proposed architecture is too complex - A feature is likely to waste more money than it creates in value - A prototype is not yet ready for production use Avoiding the wrong architecture often saves more time and money than writing additional code later. You can expect clear communication, visible progress, documented decisions, honest risk analysis, and production-focused validation. I stay involved through production readiness and engineering handoff, not only prototype delivery. I am a strong fit when AI must interact with real users, business data, external tools, and production infrastructure. Send me your architecture, requirements, repository context, or production problem. I will review the context, identify the highest-risk area, and recommend the most practical starting point.