Hayk Harutyunyan
Verified Expert in Engineering
Software Developer
London, United Kingdom
Toptal member since April 17, 2022
Hayk is an engineering leader combining infrastructure expertise (Kubernetes, AWS) with applied AI engineering. He is experienced in productionizing agentic systems: moving beyond demos to build reliable, observable, and cost-efficient AI architectures. Hayk has a unique background in FinOp and cost-aware system design.
Portfolio
Experience
- FastAPI - 13 years
- Python - 8 years
- APIs - 6 years
- Amazon Web Services (AWS) - 5 years
- Django - 5 years
- Microservices - 4 years
- SQLAlchemy - 2 years
- React - 2 years
Preferred Environment
Vim Text Editor, Amazon Web Services (AWS), Claude Code
The most amazing...
...thing I built was a user behavior simulation engine for A/B tests that achieved over 80% correctness when compared with real world results.
Work Experience
Co-founder
Relaunch
- Built a production agentic AI orchestration system at Relaunch with planner/executor/evaluator workflows, tool use, checkpointed resumption, multi-model routing, and Postgres-backed state.
- Architected a large language model (LLM) user behavior prediction engine, successfully modeling statistical outcomes of A/B tests with over 80% correlation to real-world data.
- Built a robust evaluation framework to benchmark simulated user fidelity against real-world analytics data.
- Developed a low-latency retrieval-augmented generation (RAG) subsystem for the product chat interface, optimizing retrieval precision on proprietary experiment datasets.
Senior Software Engineer
nOps
- Led a team of six to create a Kubernetes workload optimization system that chooses the most cost-optimal instances for a given set of workloads, consisting of a tool control dashboard together with a complex back end of APIs and automations.
- Built several microservices from scratch and contributed to others, leveraging FastAPI, Kafka, and Kubernetes for inter-service communication and scaling.
- Contributed to designing and implementing distributed data processing pipelines, such as event-based near real-time (NRT) ingestion and processing of AWS or Kubernetes metadata.
Software Engineer
Bmat
- Contributed to the development of a data processing pipeline that automated the processing of digital sales reports from music streaming companies (e.g., Spotify) and generated royalty claim files on behalf of musical work rights holders.
- Operated a data processing pipeline running on AWS Batch that processed digital sales reports to royalty claim files, spotting and fixing bugs, tuning issues, and other things.
- Maintained a Django-based platform that parses, ingests, and processes large volumes of file-based data with multiple processing steps and varying configurations per customer.
Software Engineer
Shopometry
- Built Facebook Ads API integrations for a retail media campaign-management platform.
- Used AWS Incognito to build an authentication and authorization subsystem.
- Build ad campaign metric reporting and performance reporting features.
Experience
A Data Processing Platform
http://pronto.bmat.comI was one of the back-end engineers maintaining and improving the pipeline. I leveraged the compute capacity of AWS Batch and Python multiprocessing libraries to build a performant system.
A Cloud Optimization Tool
http://nops.ioRelaunch
http://relaunch.aiEducation
Bachelor's Degree in Liberal Arts
University of Oxford - Oxford, UK
Bachelor's Degree in Liberal Arts
Middlebury College - Middlebury, Vermont, USA
Certifications
FinOps Certified Practitioner
FinOps Foundation
AWS Certified Developer
Amazon Web Services
Skills
Libraries/APIs
Django ORM, Pydantic, Google Ads API, SQLAlchemy, OpenAI API, Redis Queue, REST APIs, Pandas, React, Claude API
Tools
Pytest, Docker Compose, Celery, Claude Code, Claude, Codex, AWS Batch
Languages
Python, SQL, Bash, Python 3, GraphQL, Go, JavaScript
Frameworks
Django, Django REST Framework, Flask, LangGraph, OAuth 2
Paradigms
REST, Microservices, Continuous Delivery (CD), Continuous Integration (CI), DevOps, RESTful Development, Model Context Protocol (MCP)
Platforms
Amazon Web Services (AWS), Linux, Docker, Kubernetes, Apache Kafka, Google Cloud Platform (GCP)
Storage
Relational Databases, PostgreSQL, Redis, MongoDB, Elasticsearch
Other
APIs, Back-end, Distributed Systems, Cloud Architecture, API Design, Integration, AI Agents, Agentic AI, Natural Language Processing (NLP), Facebook Ads, FastAPI, CI/CD Pipelines, Data Visualization, Large Language Model Operations (LLMOps), Data Engineering, Artificial Intelligence (AI), Cost Reduction & Optimization (Cost-down), FinOps, Large Language Models (LLMs), Full-stack, Agentic AI Systems, Software Architecture, Gemini API, Leadership, API Integration, LangChain, Cloud, CTO, Engineering, RAG Systems, Retrieval-augmented Generation (RAG), Infrastructure as Code (IaC), Code Review, AI Architecture, AI Agent Orchestration
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