Hire the Best AIOps Specialists

I help growing AI teams and SaaS companies fix AWS issues that slow releases, waste cloud budget, and put production at risk. Best fit for teams that need senior DevOps support for a launch, migration, infrastructure stabilization, AWS backend improvement, backend AWS architecture, or ongoing production ownership. I can support your team as an AWS DevOps Engineer, devops engineer, cloud engineer, aws architect, and senior delivery owner across Amazon Web Services, kubernetes, ci/cd, github actions, gitlab, Git, AWS Terraform, AWS Kubernetes, Azure DevOps, GCP DevOps, and terraform-based infrastructure. I don't just "run DevOps". Being Solution Oriented, I find the 20% of infrastructure issues causing 80% of failures and overspending: unstable pipelines, slow builds, noisy deployments, mis-sized compute, always-on non-prod environments, missing alerts, weak access controls, unsafe infrastructure changes, backend bottlenecks, full stack developer handoff issues, and weak collaboration between application and infrastructure teams. You get a clear action plan, quick wins in the first days, and production-safe improvements shipped via Terraform, AWS Terraform, CI/CD, ci cd pipelines, rollback-ready delivery, and practical DevOps Engineer execution using AWS Kubernetes, Microsoft Azure, azure data factory and Google Cloud Platform. What you get from a cloud infrastructure optimization engagement: ✅ 5x faster releases via stable CI/CD, ci cd workflows, and AWS Terraform driven delivery ✅ Up to 40% AWS cost reduction by eliminating waste and rightsizing resources ✅ Safer AWS migrations with minimal downtime and rollback plans ✅ Scalable, production-ready cloud infrastructure for growing SaaS and AI products utilizing AWS Kubernetes ✅ 99.9% uptime mindset: monitoring, backups, health checks, and safe rollouts across platforms ✅ Better AWS backend stability for backend, backend AWS, and API-driven platforms ⚙️ How I work as an AWS DevOps Engineer: 1️⃣ Discovery & Assessment: analyze your current Amazon Web Services, Linux servers, backend, kubernetes, AWS ECS, AWS Lambda, AWS EKS, EKS, Amazon EKS, GitLab, GitHub Actions, AWS Terraform, AWS Kubernetes, and deployment pain points 2️⃣ Plan: define clear scope, milestones, risks, and the fastest first win across your infrastructure 3️⃣ Implement: build and automate with AWS DevOps Engineer best practices, DevOps Engineer delivery standards, cloud engineer ownership, and clean documentation 4️⃣ Verify + handover: release runbook, rollback path tested, monitoring validated, and short documentation for your team I mainly work with AWS, but I can also support mixed-cloud teams where Microsoft Azure pipelines, Azure Terraform, Azure Cloud Engineer workflows, Azure infrastructure, Google Cloud Platform, AWS VMware, or migration planning are part of the environment. My expertise covers integrating AWS Terraform and AWS Kubernetes into multi-cloud setups. Real examples: Non-Production Cloud Cost Reduction (~30%) Problem: Dev and test environments were running 24/7, burning budget overnight with no business value. 🎯 Result: Reduced non-production AWS spend by ~30% through AWS DevOps Engineer cost review, rightsizing, and automated start/stop scheduling with zero production impact. CI/CD for AWS Lambda (Python/Node.js) & Web Apps Built 30+ pipelines as an AWS DevOps Engineer for AWS Lambda build and deploy workflows with unit tests and security gates, integrating them with Jenkins, Azure DevOps and GCP DevOps workflows. 🎯 Result: New AWS Lambda services, as well as JavaScript, React, WordPress, Laravel and MySQL backend stacks, can be provisioned from a template in minutes with consistent standards. AWS Terraform + runbooks Built AWS infrastructure as IaC with terraform modules, safe plan/apply workflows, AWS Kubernetes clusters, Ansible configuration, and devops bash automation. 🎯 Result: Performance environments provisioned in ~10 minutes to reduce AWS spend. Dagster Cloud Hybrid Agent on AWS ECS As a Senior DevOps Engineer, I built a Docker image and ci cd pipeline with GitHub Actions to deploy an agent into a client AWS account. 🎯 Result: Self-serve deployments and automated dataflow runs. IBM Cloud to AWS migration Designed architecture as aws architect, AWS DevOps Engineer, and cloud engineer. Rebuilt with kubernetes, AWS EKS, EKS, Amazon EKS, and Terraform. 🎯 Result: Production-ready foundation using AWS Kubernetes. Redshift cross-account migration Migrated cluster as devops engineer with data engineer handoff support. 🎯 Result: Reliable recovery runbook for faster response. Additional environments supported: Azure DevOps pipelines, Azure Terraform, Azure Cloud Engineer support, GCP DevOps, AWS VMware, AWS Terraform, full stack developer issues, backend stabilization, backend AWS architecture, and data engineer infrastructure. ☎️ Message me a short description of your current pain for fast Senior AWS DevOps Engineer support.

Deployed 100+ cloud infrastructures Reduced deployment time by 70% for SaaS clients Senior DevOps Engineer with 7+ years of experience designing, scaling, and optimizing cloud infrastructure for startups and growing products. I specialize in building production-grade systems on AWS and Kubernetes that are reliable, cost-efficient, and easy to operate. My work focuses on eliminating downtime, improving deployment speed, and maintaining predictable infrastructure as systems scale. What I Deliver: Cloud Infrastructure (AWS / GCP / Azure) Architect and manage secure, scalable environments using best practices. Kubernetes & Containerization Production-grade deployments on EKS, GKE, AKS with Docker-based workflows. Infrastructure as Code (IaC) Terraform / CloudFormation setups for repeatable, version-controlled infrastructure. CI/CD Automation Fast and reliable pipelines using GitHub Actions, GitLab CI, Jenkins. Cost Optimization Proven track record of reducing cloud costs by 30–40% without performance impact. Monitoring & Reliability End-to-end observability using Prometheus, Grafana, ELK, CloudWatch. Impact from Recent Projects: Reduced AWS infrastructure cost by 40% through rightsizing and architecture changes Improved deployment speed by 3x using optimized CI/CD pipelines Maintained high-availability production systems (99.9%+ uptime) Migrated legacy systems to cloud-native architectures with zero downtime How I Work: Focus on long-term stability, not quick fixes Clear communication and ownership of infrastructure Design systems that scale without constant firefighting If you need a DevOps engineer who can own your infrastructure, reduce risk, and scale your system cleanly, we should discuss your project. End-to-end system design DevOps + Development ownership Scalable full-stack architecture Cloud-native application development Infrastructure + application optimization DevOps | Kubernetes | Docker Terraform | CI/CD | Amazon Web Services Microsoft Azure | Google Cloud Platform | AWS Lambda AWS Server Migration | Jenkins | Node.js React | API Integration | DevOps Engineering AI Development | Azure DevOps | Cloud Architecture Infrastructure as Code | Amazon EC2

20-year Solution Architect and Fractional CTO helping Series A–C SaaS scale-ups and Fortune 500 teams ship cloud-native, AI-powered systems that don't break at 10× load. DevOps, Software Architecture, AI consulting — delivered, not theorized. If your roadmap has any of these on it — a cloud migration that's slipping, an AI/LLM feature that demos well but won't survive production, a monolith you need to break up without freezing the product team, or a DevOps platform that's burning cash and on-call time — I've shipped that exact engagement before, more than once. What I do (and have done for 20 years): ✅ Solution & Software Architecture — system design, domain modeling, microservices vs. modular monolith trade-offs, event-driven & API-first platforms, architecture reviews and ADRs your team will actually follow ✅ DevOps, Cloud & Platform Engineering — AWS / Azure / GCP, Kubernetes (EKS/AKS/GKE), Terraform, CI/CD, FinOps cost reduction, SRE / observability, zero-downtime migrations ✅ AI Consulting & AI Solutions Architecture — production LLM / RAG systems, agentic workflows, AI strategy roadmaps, build-vs-buy decisions, model selection, evaluation harnesses, vector DBs, secure enterprise AI ✅ Fractional CTO & Tech Due Diligence — for founders pre-Series A through Series C; for PE / VC pre/post-investment For startups & scale-ups: I move at founder-speed. MVP architectures that won't need a rewrite at Series B, AI features you can actually ship, hiring plans for your first 5 engineers, and an architecture roadmap your investors will respect. For enterprises: I bring TOGAF-style rigor without the consultancy bloat — solution architecture, cloud migration strategy, vendor-neutral AI roadmaps, security/compliance-aware design (SOC 2, ISO 27001, HIPAA, GDPR), and stakeholder-ready documentation. Selected outcomes from the last 20 years: 🏆 Cut cloud spend 41% and shortened deploy time from 45 minutes to under 5 minutes for a SaaS platform serving 12M users 🏆 Designed the AI/RAG architecture that took a B2B SaaS from PoC to production with 30+ enterprise customers in 5 months 🏆 Re-architected a legacy monolith into modular services with zero customer-facing downtime over 7 months 🏆 Led DevOps platform builds (Terraform + Kubernetes + GitOps) that cut on-call pages by 70% and onboarding from weeks to days How I work: small commitments first. Most engagements start with a paid 60-minute architecture call or a 1–2 week assessment, so we both know it fits before you commit to a long retainer. I write things down. I document decisions. I leave your team better than I found it. What I won't do: rewrite-the-world projects with no executive sponsorship, "just do everything yourself" engagements with no engineering team to hand off to, or AI demos that aren't on a path to production. Send me a paragraph about your stack, your team size, and the outcome you need in the next 90 days. I'll come back inside 24 hours with a short, specific point of view not a sales pitch. If we're a fit, we'll book a working call from there.

Most ML models never make it to production. Teams get stuck between "it works in the notebook" and "it runs reliably at scale." That's exactly the gap I close. I'm Hari — an MLOps and AI engineer who builds the full pipeline around your model: containerization, CI/CD, monitoring, RAG systems, and LLM deployment. One completed project, one 5-star review — and I'm looking for the next client who needs this done right. What I've shipped: SmartMeter Analytics Platform — Built a full-stack system processing 15-minute interval data from 36 IoT meters. FastAPI backend running live anomaly detection (Z-score), 24-hour load forecasting, and voltage quality analysis on PostgreSQL data — delivered to a 13-page React dashboard with real-time KPIs and solar simulation. End-to-End MLOps Pipeline — Designed and deployed three production ML models (flight price prediction, gender classification, hotel recommendations) with full experiment tracking via MLflow, a Flask REST API, Docker containerization, Kubernetes auto-scaling, Airflow data pipelines, and Jenkins CI/CD. My production stack: Python | FastAPI | Docker | Kubernetes | MLflow | Apache Airflow | LangChain | PostgreSQL | CI/CD | RAG pipelines | TensorFlow | PyTorch Where I add the most value: - Deploying your existing ML model as a scalable REST API - Building RAG systems and LLM-powered applications with LangChain - Setting up MLOps infrastructure (experiment tracking, model versioning, retraining pipelines) - Migrating data pipelines from notebooks to production-grade Airflow workflows If you're building something in this space, tell me what you're working on. I'll give you a direct answer within 24 hours on whether and how I can help — no fluff, no commitment needed.

I build production-ready AI systems and scalable cloud infrastructure for startups and businesses. My expertise combines AI engineering, backend systems, Kubernetes, and DevOps to deploy reliable LLM applications, AI SaaS platforms, RAG pipelines, and inference infrastructure that can scale in production. I focus on real-world AI engineering, not just prototypes. That includes designing systems that are scalable, observable, cost-efficient, secure, and easy to operate under production workloads. I have experience working with: - Generative AI applications & AI SaaS platforms. - AI agents & autonomous workflows. - RAG pipelines & vector search systems. - Voice AI & speech processing systems. - Fine-tuning & custom model workflows. - LLM serving, inference optimization & GPU deployments. - AI orchestration & automation pipelines. - FastAPI & Golang backend services. - REST APIs, WebSockets & streaming systems. - Distributed systems & microservices architecture. - Kubernetes-based AI platforms & cloud infrastructure. - CI/CD pipelines & Infrastructure as Code. - Observability, monitoring & distributed tracing. - Autoscaling, performance optimization & production reliability. - Cloud-native deployments on AWS, GCP & self-hosted environments. 𝗔𝗜 / 𝗟𝗟𝗠 / 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜: OpenAI API, LangChain, LangGraph, CrewAI, AutoGen, Hugging Face, Transformers, vLLM, Ollama, LLaMA, Mistral, Whisper, Faster-Whisper, RAG pipelines, vector databases, prompt engineering, AI orchestration, fine-tuning workflows, inference optimization. 𝗕𝗮𝗰𝗸𝗲𝗻𝗱 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴: Python, Golang, FastAPI, Flask, Django, REST APIs, gRPC, WebSockets, streaming APIs, distributed systems, microservices architecture. 𝗗𝗲𝘃𝗢𝗽𝘀 / 𝗖𝗹𝗼𝘂𝗱 / 𝗟𝗟𝗠𝗢𝗽𝘀: Kubernetes, Docker, Helm, Terraform, ArgoCD, GitHub Actions, AWS, GCP, Hetzner, CI/CD pipelines, Infrastructure as Code, GPU deployments, autoscaling, cloud-native architecture. 𝗢𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗶𝗹𝗶𝘁𝘆: Prometheus, Grafana, OpenTelemetry, distributed tracing, centralized logging, monitoring pipelines. 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀: PostgreSQL, MySQL, Redis, MongoDB, ClickHouse, Firebase, vector databases. 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻: Playwright, Selenium, Scrapy, BeautifulSoup, workflow automation, data pipelines. If you need someone who can architect, deploy, and scale AI systems end-to-end, from AI applications and backend APIs to Kubernetes infrastructure and production operations, I can help.

I help SaaS companies build AI-powered backend systems, automation pipelines, and scalable APIs that actually go to production. If you're working on: * LLM / RAG pipelines (OpenAI, embeddings, vector DBs) * AI automation workflows * Backend systems that need to scale reliably * SaaS products with real users and real load I can help you design, build, and ship it properly. --- 🔧 What I specialize in: * AI Systems: RAG pipelines, LLM integrations, async processing * Backend Engineering: Node.js, TypeScript, Python (FastAPI) * Scalable Architecture: Microservices, queues (SQS, RabbitMQ, Redis), event-driven systems * Cloud & DevOps: AWS, Docker, CI/CD pipelines --- 🚀 Selected Work: ✔ Built an AI-powered PDF processing system that reduced manual validation work by ~90% and automated print workflows end-to-end. ✔ Developed a high-scale AI compliance SaaS (RAG + vector search) processing 25,000+ async tasks/hour. ✔ Architected a full CRM + business platform that streamlined operations and financial tracking for a real-world firm. --- 💡 What you get working with me: * Clean, production-ready code (not prototypes) * Clear system design thinking * Fast communication & ownership mindset --- If you're building something serious and need it done right, send me a message.