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in Armenia

Most data and machine learning projects do not fail because the models are wrong. They fail because real decisions are built on systems that were never designed for production reality. Data distributions change. Assumptions stop holding. Feedback loops appear once decisions are automated. What looks correct during analysis can quietly become wrong when it starts affecting real users, money, or operations. My work focuses on that gap between analysis and real-world execution. I design and implement production decision systems, not demos, notebooks, or isolated models. These are systems where predictions directly drive actions such as approvals, rejections, prioritization, allocations, or automated workflows. In this context, correctness, transparency, and operational control matter more than theoretical performance. My background combines applied machine learning, data engineering, and direct ownership of real decision pipelines. I have worked on systems used in environments where mistakes are costly, including credit decisioning and automated trading. I approach every project assuming that data will be imperfect and conditions will change over time. Models are components of a system, not the system itself. I work system-first. That means clearly defining decision logic, data contracts, monitoring signals, and conditions for retraining or rollback. A system should surface degradation early, before it causes damage, not after. What I do not do is equally important. I do not sell AI magic, guaranteed outcomes, or opaque black-box solutions. I do not optimize offline metrics without considering how the system behaves once deployed. If a system cannot be monitored, understood, and controlled in production, I do not consider it complete. Clients usually reach out to me when decisions matter, scale is real, and failure is expensive. If you need a system that behaves predictably under pressure and you want to clearly understand both its capabilities and its limits, we are likely a good fit.

I help engineering teams and technical founders make the right system decisions before they become expensive — scoping what to build, de-risking architecture before irreversible choices are made, and owning delivery through to scale. My background spans cloud architecture, solution architecture, and database systems across AWS and Azure. The combination matters: most production failures I have seen trace back not to bad code, but to structural decisions made early in the schema, the system boundaries, or the deployment model — that compounded quietly until they could not be undone. I am based in Armenia and work async-first with clients across the US, Europe, and Australia. Architecture work — system design, technical reviews, decision documentation, governance frameworks — does not require timezone overlap. My clients do not experience geographic friction. 🔹 𝐓𝐡𝐫𝐞𝐞 𝐫𝐞𝐜𝐞𝐧𝐭 𝐩𝐫𝐨𝐣𝐞𝐜𝐭𝐬 𝐭𝐡𝐚𝐭 𝐜𝐚𝐩𝐭𝐮𝐫𝐞 𝐡𝐨𝐰 𝐈 𝐰𝐨𝐫𝐤: 𝐆𝐃𝐂 𝐌𝐞𝐝𝐢𝐚 (𝐈𝐫𝐞𝐥𝐚𝐧𝐝) — built an LLM-assisted database audit engine running across 3 MySQL clusters and 15 production nodes. 13 independent checkers feed findings to the LLM, which grades each as Critical / Major / Minor / Trivial and writes a short, grounded explanation. Runs hands-off 365 times a year. 𝐇𝐑𝐒 𝐆𝐫𝐨𝐮𝐩 (𝐂𝐨𝐥𝐨𝐠𝐧𝐞, 𝐆𝐞𝐫𝐦𝐚𝐧𝐲) — designed and built financial data pipelines on AWS that bridge MS Navision (ERP) and SAP through a single canonical model. Safe to replay, with scheduled reconciliation. 𝐆𝐥𝐚𝐬𝐬𝐰𝐚𝐥𝐥 𝐒𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐬 (𝐔𝐊) — Cloud Engineer on the Content Disarm and Reconstruction platform now trusted by NATO and U.S. and U.K. national security agencies. Helped adapt the platform for AWS EKS, Azure AKS, and K3s, and built the file ingress orchestration plugin handling terabytes of potentially infected files. 🧭 𝐇𝐨𝐰 𝐈 𝐰𝐨𝐫𝐤 I don't approach projects as feature delivery. I approach them as temporary or ongoing technical ownership — short engagements that clarify direction, longer ones that carry the system through to scale. The shape is the same in both: • Reduce MVP scope while keeping the core value loop intact • Define cloud architecture and system constraints before implementation begins • Make decisions explicit instead of accumulating quiet assumptions • Separate short-term delivery from long-term structural risk • Hand off to in-house engineers cleanly when the moment is right Some engagements take two weeks. Some turn into multi-year work. The goal in both is the same — systems that don't need heroics to keep running. 🎯 𝐖𝐡𝐞𝐫𝐞 𝐩𝐫𝐨𝐣𝐞𝐜𝐭𝐬 𝐮𝐬𝐮𝐚𝐥𝐥𝐲 𝐠𝐞𝐭 𝐬𝐭𝐮𝐜𝐤 In early- and growth-stage systems, the biggest problems are rarely about tooling or raw engineering skill. They are decision gaps: • Feature lists that grow faster than clarity • AI is treated as a black box instead of a constrained system • Developers hired before the success criteria are defined • Architectural decisions deferred until they're expensive to reverse • "We'll fix it later" choices that quietly become permanent That's the layer I focus on stabilizing first — so execution stays fast without becoming brittle. ⛁ 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞 𝐚𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 & 𝐝𝐢𝐚𝐠𝐧𝐨𝐬𝐭𝐢𝐜𝐬 Production databases are where architectural decisions surface most visibly — slow queries, bloated indexes, configuration drift, schema choices that worked at 10K rows and break at 10M. I have extensive hands-on experience with PostgreSQL, MySQL, and MariaDB in high-load production environments. For teams that need a fast, structured assessment: I offer a fixed-price SQL database audit via my Project Catalog — a prioritized diagnostic report delivered in 1–2 days, ranked by business impact. 🧱 𝐒𝐭𝐚𝐜𝐤 • Databases — PostgreSQL, MySQL, MariaDB, OpenSearch, Elasticsearch, DynamoDB • Cloud — AWS, Azure, Kubernetes, Docker, serverless • Security — IAM, encryption, access boundaries, secure data flows • DevOps — CI/CD, GitHub actions, CloudFormation, observability • AI-adjacent — guardrails, confidence gating, deterministic vs. generative boundaries Technology serves decisions, not the other way around. 📅 𝐄𝐧𝐠𝐚𝐠𝐞𝐦𝐞𝐧𝐭 𝐟𝐨𝐫𝐦𝐚𝐭𝐬 • Long-term architecture and delivery ownership • Fractional CTO or technical advisor • Short-term advisory (1–4 weeks) • Fixed-price SQL database audit (Project Catalog — from $750) Most successful collaborations start small and grow naturally once alignment is in place. 📨 𝐋𝐞𝐭'𝐬 𝐭𝐚𝐥𝐤 If you are unsure what to build, what to cut, or how to de-risk a critical phase — or if your database is showing signs of trouble — a short conversation usually makes sense. From there, we decide together on the right shape of engagement.

Hello I am a Systems Engineer with over 16 years of diverse experience in DevOps. I specialize in diagnosing and restoring broken, over-engineered, or underperforming infrastructures—both legacy and cloud-based—by delivering highly effective solutions in DevOps.

I am a Data Scientist with about 5 years of experience specializing in product analytics, A/B testing, and machine learning. Overall, I have more than 15 years of on-site work experience. I am detail-oriented, reliable, and organized, and I always make sure my work is completed accurately and on time. My expertise includes Python, SQL, and data visualization tools like Looker and Databricks. I excel at transforming complex product data into actionable insights, enabling informed decision-making that drives engagement and conversion. I have successfully designed and analyzed experiments focusing on funnel performance and user behavior across mobile and web applications. My analytical skills allow me to evaluate app features using comprehensive metrics and dashboards. If you are seeking a reliable partner to enhance your product strategy through data-driven insights, I am ready to collaborate and help elevate your results to the next level.

💎 Python 💎 SQL 💎 AWS, Spark, API, LLM ⚒️ ETL ⚒️ BI ⚒️ Data Warehouse 🏢 American insurance company with more than 50 AMS sources with different types of integration and structure unified to single data model ➡️ 2022 – 2026 Data Engineer 🏢 UK Logistics company processing returns from top retailers in Europe ➡️ 2021 – 2022 Database Administrator – solely built and maintained MS SQL database to support daily ETL and analytics processes 🏢 Top 4 telecommunication provider in Russia ➡️ 2020 – 2022 BI Developer – built and maintained the environment with more than 200 daily ETL streams, end-to-end development (from external data source to corporate DWH and dashboard) ➡️ 2018 – 2020 Data Analyst – analysis and insights for country-wide sales and system processes

🎓 8+ years of experience 🏆 Senior Full-stack developer 🌟 TOP RATED PLUS Freelancer 💯 100% JOB SUCCESS 💼 200+ Successful Projects I'm a professional 𝐏𝐇𝐏/𝐋𝐚𝐫𝐚𝐯𝐞𝐥 and 𝐍𝐨𝐝𝐞.𝐣𝐬 Specialist with excellent front-end skills using 𝐕𝐮𝐞.𝐣𝐬 and 𝐑𝐞𝐚𝐜𝐭.𝐣𝐬 frameworks. ✔️ 𝗨𝘀𝗲𝗿 𝗜𝗻𝘁𝗲𝗿𝗳𝗮𝗰𝗲: (HTML5/CSS3/JavaScript/Typescript/Vue.js/Vuex/React.js), ✔️ 𝗕𝗮𝗰𝗸𝗲𝗻𝗱 & 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗟𝗼𝗴𝗶𝗰: (PHP, Node.js, Laravel, Eloquent Model, MVC architecture, OOP, RESTful API, 3rd party web services), ✔️ 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲 𝗺𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁: (MySQL, PostgreSQL, MongoDB), ✔️ 𝗦𝗲𝗮𝗿𝗰𝗵 & 𝗦𝗶𝗺𝗶𝗹𝗮𝗿𝗶𝘁𝘆 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀: (Elasticsearch for full-text search, and Faiss for fast, efficient similarity search). I feel I'm highly skilled in each layer of development and I also have a great deal of infrastructure experience to deploy, secure, and maintain an application. I have worked extensively with 𝐀𝐏𝐈𝐬 in a variety of situations. I have created APIs for other services to consume, and I have worked with a wide variety of public and private APIs including 𝘄𝗲𝗮𝘁𝗵𝗲𝗿, 𝗳𝗶𝗻𝗮𝗻𝗰𝗲, 𝘁𝗿𝗮𝗳𝗳𝗶𝗰, 𝘀𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗮𝗻𝗱 𝗲𝗖𝗼𝗺𝗺𝗲𝗿𝗰𝗲 API services. 🚀 𝗟𝗲𝘁'𝘀 𝗯𝘂𝗶𝗹𝗱 𝘀𝗼𝗺𝗲𝘁𝗵𝗶𝗻𝗴 𝗲𝘅𝗰𝗲𝗽𝘁𝗶𝗼𝗻𝗮𝗹 𝘁𝗼𝗴𝗲𝘁𝗵𝗲𝗿! Feel free to contact me to learn more about my work. Thanks, 👨‍💻