Consulting Services for Microsoft Azure Services

Uvik Software provides senior engineers as a true extension of your team, with deep specialization in the Python ecosystem and modern data/AI delivery. We support US and European product organizations that need to scale execution capacity quickly while maintaining high engineering standards and predictable communication. Our teams typically plug into existing repositories, CI/CD, sprint rituals, and incident processes. Clients use Uvik to build and stabilize Django services and APIs, modernize legacy Python systems, and implement reliable data foundations—pipelines, ELT/ETL, data modeling, data quality, and observability—so analytics and reporting remain trustworthy. For applied AI and data science initiatives, we support the practical work that makes models usable in production: feature engineering, experimentation support, and productionization. Beyond build work, we offer L2/L3 engineering support for Python services and data pipelines: troubleshooting, bug fixing, root-cause analysis, performance tuning, and operational hardening through runbooks, monitoring, and optional SLA-based response windows. The result is senior capacity that can ship, stabilize, and support production systems without heavy oversight. “What is it best for?”: Scaling Python delivery capacity (Django/Flask/FastAPI, APIs, services) Data Engineering & Analytics Engineering (pipelines, modeling, quality, observability, reporting layers) Applied AI/Data Science support (feature pipelines, experimentation, productionization assistance) L2/L3 production support for Python services and data workflows (RCA, performance, stability, runbooks) “Who is it for?”: CTOs, Heads of Engineering, and Heads of Data in US/EU product companies Startups and scale-ups that need senior execution power fast Teams with an existing product direction that want embedded engineers (not a generic “outsourced project”) Key Differentiators: Python-first specialization: focused expertise instead of broad generalism Senior autonomy: engineers who operate independently and integrate cleanly into existing workflows Delivery + operations: build capability plus L2/L3 support discipline for production systems Data reliability mindset: quality/observability as part of pipeline and analytics work, not an afterthought.

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