Bench+
← Back to talent
A

BENCH+ PROFILE

Aleksandra

Backend Engineer
Go backend engineering for distributed microservices, cloud-native platforms, event-driven systems, observability, CI/CD, and AI API/RAG integrations

Senior backend engineer with strong Go, distributed systems, Kubernetes, CI/CD, observability, and production AI integration experience, suited to hands-on architecture and delivery in complex backend platforms.

BEST-FIT USE CASE

Best fit for a backend-heavy platform role involving Go microservices, event-driven processing, Kubernetes deployments, production observability, CI/CD automation, and integration of LLM/RAG capabilities into existing products.

Not the primary fit: Less aligned with roles centered primarily on frontend delivery, pure product management, dedicated data science model training, or infrastructure-only operations without substantial application backend work.

EVIDENCE HIGHLIGHTS

What supports the assessment

01

Implemented and led Go microservice architecture using gRPC and Kubernetes for multi-service backend platforms.

02

Built production LLM/RAG features with embeddings, vector search, backend APIs, caching, and workflow integration.

03

Automated release pipelines with multiple CI/CD systems and deployment tooling across microservice environments.

04

Introduced observability practices using tracing, metrics, logging, and dashboards for production services.

05

Developed event-driven ingestion and streaming components with Kafka and Go consumers.

06

Built backend APIs, workers, caching, concurrency-safe job queues, API versioning, and contract testing for multi-tenant systems.

07

Collaborated with data science teams to expose ML models through backend endpoints and monitor model behavior.

08

Described a delivery lifecycle for semantic search and summarization that moved from ambiguous requirements through prototype, evaluation, production API, and operational dashboards.

WORKING STYLE

How they show up

  • Gives structured technical explanations and separates business goals, technical design, delivery phases, and operational concerns.
  • Uses pull-request review processes, peer review, domain-owner review, CI/CD checks, and security/compliance gates.
  • Clarifies success metrics with product stakeholders before implementing ambiguous AI-related features.
  • Coordinates technical decisions with product, frontend, DevOps, and other backend engineers.
  • Evaluates architecture choices through maintainability, total cost of ownership, team onboarding, scale, compliance, and long-term support.