Production Engineer · AI Workflow Integration · Business Systems Architect
I help companies design, build, improve and scale real business systems with practical AI integration, automation, internal tools, backend delivery, cloud infrastructure and production reliability.
I am not an AI hype consultant or a thin-wrapper builder. I am a real engineer with 10+ years of experience building systems used in real business operations. I can help you identify where AI can remove manual work, connect it to your existing tools and data, build reliable workflows around it, and make it useful for the people doing the work every day.
Worked with high-load systems in enterprise environments, built and optimized many architectures
Specialized in solving complex backend, cloud and integration problems for business-critical systems
Hands-on experience with AWS services, production infrastructure, deployment reliability and cost optimization
Production experience with JVM services, secure APIs, cloud integrations and maintainable backend systems
LLM workflows, RAG, automation, frontend delivery and backend integration grounded in real production engineering
I help companies integrate AI into daily work, automate operational processes, modernize systems and build production software that supports real business operations.
Identify where AI can speed up real work, connect it to existing tools and data, design safe human-in-the-loop workflows, and turn experiments into usable operational systems.
Design and build complete business systems across frontend, backend, cloud infrastructure, integrations, AI workflows, release flow and production operations.
Review existing platforms, identify structural problems, reduce technical risk and create a practical modernization path that can include automation and AI where it pays off.
Review infrastructure for scalability, security, cost efficiency, resilience and maintainability across AWS services and production workloads.
Build practical internal tools, admin systems and workflow automation that reduce manual work, speed up routine decisions and fit the way the business actually operates.
Connect products, CRMs, ERPs, databases, backend services and third-party APIs with reliable orchestration, permissions and operational visibility.
Design LLM, RAG and tool-based systems with usable interfaces, backend orchestration, guardrails, monitoring and clean integration into real business operations.
Diagnose production failures, performance bottlenecks and operational risks using logs, metrics, architecture analysis and senior engineering judgment.
Custom enterprise engagements available
Practical engineering for real business systems: AI-assisted workflows, backend orchestration, internal tools, cloud, automation and integrations that create measurable value.
LLM APIs, local models, LLM workflows, RAG, prompt design, tool calling, structured outputs, AI adoption
PostgreSQL, pgvector, Elasticsearch, document ingestion, semantic search, business data integration
Java, Kotlin, Groovy, Python, frontend interfaces, backend services, REST APIs, background jobs
Logs, metrics, tracing, dashboards, alerting, AI workflow monitoring, production debugging
AWS EC2, ECS, Lambda, RDS, DMS, CloudFormation, Docker, CI/CD, secure deployment pipelines
Architecture decisions, cost optimization, reliability reviews, incident analysis, maintainable automation
The SUCCESS principles guide how I build systems that stay useful, understandable and maintainable after they reach production.
Processes should grow with the business instead of creating more manual coordination.
Teams should be able to reason about how the system works, fails and changes.
Core business logic, integrations and infrastructure should have clear responsibilities.
Automation should be observable, reversible and aligned with real operational rules.
Systems should reduce repetitive work, delays, cost and unnecessary complexity.
APIs, data flows and user actions should be protected by practical controls.
Good engineering should make the next change easier, not more fragile.
Design of scalable and reliable systems
EC2, ECS, DMS, Lambda, RDS, CloudFormation
Authentication, authorization, secure APIs and production risk reduction
Pipeline optimization, automation, monitoring
Issue diagnosis, performance tuning, debugging
Snowflake, dbt, ECS, data migration
Strategy, planning, migration implementation
AI adoption, LLM integration, RAG, workflow automation, internal copilots and production AI features
From frontend experience to backend architecture and production deployment
AI workflow integration, automation, architecture, cloud, internal tools and production engineering — built properly for daily operations.
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