Systems thinking
I design backend systems from the operational level up: data flow, failure modes, throughput constraints, observability, and maintainability.
For Employers
Artur Tsarikovich Senior Backend & AI Engineer
Backend engineer with a strong record in scalable systems, cloud-native architecture, ML infrastructure, and production-grade delivery.
Best fit for roles where backend depth, reliability, ownership, performance, and practical AI experience all matter at the same time.
Senior Backend Engineer
Platform / Infrastructure
Applied AI / ML Systems
Core strengths
Why hiring teams tend to trust my profile
Applied AI without hype
I focus on production usefulness: inference pipelines, vector search, evaluation, lifecycle automation, and measurable cost control.
Performance and reliability
Async processing, multiprocessing, data pipeline optimization, and release workflows that reduce operational friction.
Ownership and clarity
I communicate clearly, document decisions, mentor where useful, and care about the long-term shape of the system, not just the next patch.
Role fit
Where I tend to create the most value
Backend
Senior Backend Engineer
For teams that need high-quality implementation with architecture awareness, clean code, and production accountability.
Platform
Platform / Infrastructure Engineer
For systems where reliability, deployment flow, data movement, observability, and operational maturity matter as much as features.
Applied AI
AI Infrastructure / Applied AI Engineer
For products that need practical AI with evaluation, orchestration, vector search, cost awareness, and a clean production path.
Selected impact
Practical outcomes, not just tool familiarity
Cost optimization
Cut Bedrock usage costs through prompt caching and evaluation improvements while maintaining quality.
Throughput increase
Increased workload throughput through async and multiprocessing orchestration on data-heavy pipelines.
Pipeline reliability gain
Automated ML lifecycle flows to remove manual intervention and improve delivery consistency.
Stable platform delivery
Delivered APIs, releases, and processing systems that remained dependable under production pressure.
Experience
Relevant experience in production environments
Backend Engineer
- Built production ML inference pipelines with AWS Bedrock and semantic relevance workflows
- Integrated OpenSearch vector search with KNN and embeddings
- Automated retraining and index updates using EventBridge and Step Functions
Backend Developer
- Maintained automated trading backend with low-latency execution paths
- Optimized ingestion and signal generation for millions of daily market updates
- Built monitoring and storage improvements for live trading operations
Backend Developer
- Migrated legacy Flask services to FastAPI and improved response times
- Scaled async request handling and containerized delivery pipelines
- Introduced engineering discipline through reviews, testing, and workflow improvements
Architecture showcases
Representative systems I can own from design to production
Applied AI
Inference, evaluation, retraining, and vector search pipeline
End-to-end flow from extraction and embeddings to index updates, relevance logic, evaluation loops, and production monitoring.
Data platform
Event-driven ingestion and processing backbone
High-volume ingestion, async orchestration, retry-safe processing, storage design, and observability-first operations on AWS.
Execution systems
Low-latency decision and execution backend
Real-time backend logic with monitoring, storage strategy, execution safety, and throughput control under live conditions.
Stack and domains
Comfortable at the intersection of backend, cloud, and applied AI
Core technologies
Python
AWS
FastAPI
Flask
PostgreSQL
Redis
Docker
Kubernetes
OpenSearch
Prometheus
Best used on teams that need
- Strong backend implementation with architecture awareness
- Someone who can navigate both infrastructure and product constraints
- A disciplined engineer who cares about clarity and long-term quality
Hiring
If you need backend depth with real production judgment
I’m open to conversations around backend, infrastructure, platform, and applied AI roles where technical quality and business impact both matter. Relevant project scope, ownership, and execution context can be shared during the process.