2022-present · Main focus
Backend, Platform & AI-native SDLC Engineering
Builds backend services, integration layers, and internal tooling around AI-assisted engineering workflows.
CV
Alex Sandruk
Backend and platform engineer working across AI-native SDLC, DevOps/SRE, workflow tooling, and technical product systems. This is not six unrelated careers: it is one compounding engineering path that moved from delivery leadership and platform operations into AI-native execution.
Career map
One path, six areas of focus
The strongest signal is the compounding: management and product systems first, platform and research depth next, then a 2022 shift into AI-native SDLC and a 2024 focus on agentic workflows.
2016-present · Parallel track, AI-boosted since 2022
DevOps, SRE & Platform Operations
Improves deployment hygiene, observability, access boundaries, and recovery paths for systems that need to keep moving under pressure.
2024-present · Main focus
AI Integration & Agentic Workflows
Turns prompt-heavy experiments into engineering systems with clearer contracts, review loops, and operator control.
2022-present · Community and podcast track
DX, DevRel & Engineering Enablement
Makes technical work easier to adopt through tooling clarity, examples, community loops, and public technical explanation.
2012-2022 · Leadership foundation
Engineering Management, TPM & Product Systems
Works well where delivery needs structure: translating across product, engineering, operations, and prioritization.
2016-present · Ongoing operating mode
Research, Market & Technical Investigation
Useful when a team needs a careful first pass on a messy technical or market question before committing to a build path.
Focus areas
Six ways I support engineering teams
2022-present · Main focus
Backend, Platform & AI-native SDLC Engineering
Builds backend services, integration layers, and internal tooling around AI-assisted engineering workflows.
- - TypeScript and Python delivery
- - Interfaces, workflow rails, and inspectable runtime behavior
2016-present · Parallel track, AI-boosted since 2022
DevOps, SRE & Platform Operations
Improves deployment hygiene, observability, access boundaries, and recovery paths for systems that need to keep moving under pressure.
- - CI/CD, environment discipline, and runbooks
- - Checks, monitoring, incident handling, and GitOps habits
2024-present · Main focus
AI Integration & Agentic Workflows
Turns prompt-heavy experiments into engineering systems with clearer contracts, review loops, and operator control.
- - Agentic workflow implementation
- - Verification gates, browser checks, and human-in-the-loop adoption
2022-present · Community and podcast track
DX, DevRel & Engineering Enablement
Makes technical work easier to adopt through tooling clarity, examples, community loops, and public technical explanation.
- - Two active communities and podcast work
- - Operational documentation and explainer-grade technical writing
2012-2022 · Leadership foundation
Engineering Management, TPM & Product Systems
Works well where delivery needs structure: translating across product, engineering, operations, and prioritization.
- - Spec shaping and execution loops
- - Cross-functional coordination and system-level tradeoff handling
2016-present · Ongoing operating mode
Research, Market & Technical Investigation
Useful when a team needs a careful first pass on a messy technical or market question before committing to a build path.
- - Technical reconnaissance
- - Decision-oriented synthesis and risk framing
Capabilities
What I work with
Core stack
- - TypeScript
- - Node.js
- - Python
- - PostgreSQL
- - Astro
- - React
Platform and delivery
- - Docker
- - Kubernetes
- - Cloudflare
- - CI/CD
- - GitOps
- - Runbooks
AI and workflow systems
- - AI integration layers
- - Agentic workflows
- - Task routing
- - Browser verification
- - Workflow tooling
Operations and visibility
- - Observability
- - OpenTelemetry
- - Checks and QA
- - Access boundaries
- - Recovery paths
Working style
- - Most useful when AI ambition meets delivery friction and someone needs to make the system legible.
- - Prefers boring, robust interfaces over impressive but fragile demos.
- - Comfortable moving between code, runtime, docs, and operator workflows in the same slice.
Reader note
This page provides a capability overview. Detailed implementation examples and technical deep dives are available in the work and writing sections.
Hiring next step
Use this CV as the capability map, then review selected work and availability.
The strongest route is CV for fit, selected-work examples for context, current focus for timing, and Contact when there is a real role or collaboration to discuss.