Data Engineering, Machine Learning & Agentic AI
With over 10 years of hands-on experience in data systems and software architecture (since 2012), Konstantin Sh. delivers high-impact analytical and AI infrastructure: centralized Cloud Data Warehouses in Google Cloud (BigQuery), Snowflake, and AWS, automated ETL/ELT pipelines with Prefect/Kestra, predictive Machine Learning (CatBoost), and production-ready autonomous Agentic AI systems. Available for remote contract work.
Transparent Time & Materials Engineering
No inflated agency retainers, no opaque scope definitions that trigger costly change orders. Dedicated senior technical execution with full autonomy and accountability.
“Engagement exclusively via technical allocation and hourly contract ($/hr) or sprint-based milestones. Bi-weekly invoicing with detailed, transparent activity reports. No hidden scopes, no surprise costs.”
01. Hourly Technical Allocation
Direct engagement with the senior specialist. You pay strictly for technical hours invested in architecture, coding, data pipelines, and automated testing, with commit-level traceability.
02. Bi-Weekly Milestones
15-day review cycles with itemized breakdown of commits, PRs, delivered pipelines, and hours invested. Predictable invoicing aligned with your development sprint cadence.
03. Total Autonomy & Ownership
Zero vendor lock-in. All source code, cloud infrastructure, databases, and pipelines live in your company accounts. You maintain 100% intellectual property from day one.
Why Hire a Senior Data & AI Architect Instead of a Traditional Agency?
Conventional agencies operate with multiple management layers, fragmented handoffs, and high billing overhead. The approach here is direct, senior-level data and AI engineering tailored to your operational reality.
- ✕Multiple layers of communication noise: Account managers and sales reps who do not grasp technical nuances, causing delays and pipeline misalignment.
- ✕Fragile scripts & unvalidated pipelines: Data workflows built without strict data contracts (Pydantic/Pandera), leading to silent failures and broken dashboards.
- ✕Superficial AI wrappers: Generic chatbot prototypes that lack validated tool-calling, unable to query databases or execute operational actions safely.
- ✕Vendor Lock-in: Severe obstacles when transferring data pipelines to in-house teams or scaling without paying ongoing monthly maintenance retainers.
- ✓Direct Engineering Communication: Decisions made at the speed of code, aligned directly with the architect designing and writing the pipelines.
- ✓Production-Grade Data Platforms: Centralized Data Warehouses in GCP BigQuery, Snowflake, and AWS with resilient Prefect/Kestra ETL/ELT pipelines.
- ✓Deterministic Agentic AI Systems: Autonomous AI agents with structured tool-calling (Pydantic AI / ADK) that query databases, invoke APIs, and automate real operations.
- ✓100% Client-Owned Code & Cloud: Repositories in your GitHub, services provisioned inside your corporate GCP/AWS accounts with clear docs.
Unified Ecosystem: From Modern Data Platform to Autonomous Agentic AI
Explore the core technical domains and review enterprise architectures delivered in production.
Data Solutions & ETL Pipelines
Modern Data Warehouses in Google Cloud (BigQuery), Snowflake, and AWS. Resilient ETL/ELT pipelines orchestrated with Prefect/Kestra and Docker, and Looker BI.
Agentic AI Solutions & Applied ML
Deterministic autonomous AI agents with structured tool-calling (Pydantic AI / ADK), predictive ML models (CatBoost), time series forecasting, and MLOps.
Real-World Case Studies
Selected enterprise initiatives across retail, aviation, and tech: greenfield BigQuery modernizations with AI agents, retail migrations for 200+ stores, B2B/B2E platforms, and cloud BI.
Ready to architect or scale your technical infrastructure?
Schedule an introductory technical discovery call. We'll examine your current bottlenecks, evaluate your stack, and establish an accurate hourly allocation estimate for your contract needs.