Software Engineer Genai (rou, Hybrid)

CrowdStrike CrowdStrike · Enterprise · Bucharest, Romania

Software Engineer role focused on building and scaling an internal Generative AI Platform, developing backend services for AI-driven solutions including RAG and agentic workflows, and operating secure LLM deployments.

What you'd actually do

  1. Design and implement scalable cloud services using Golang in a cloud-native environment
  2. Build reusable platform components and frameworks that other teams can leverage for their GenAI use-cases
  3. Develop and operate secure LLM deployments in both cloud and on-prem environments
  4. Create deep integrations with internal systems and establish frameworks for future connectors
  5. Build high-performance data processing pipelines for document handling and RAG implementations

Skills

Required

  • 4+ years of software engineering experience
  • Strong proficiency in Golang or a similar backend language
  • Deep understanding of cloud-native architectures and microservices
  • Experience with AWS services and cloud infrastructure, including technologies such as Kafka, Cassandra, ElasticSearch
  • Practical knowledge of operating production services - CI/CD, monitoring (Grafana, Prometheus), and Kubernetes
  • Strong grasp of multithreading, concurrency, and parallel processing
  • Experience in designing, building and maintaining REST APIs
  • Understanding of engineering best practices - code reviews, testing, resilient architecture
  • Excellent problem-solving and debugging skills
  • Strong written and verbal communication skills

Nice to have

  • Experience with LLM integrations or AI/ML platforms
  • Knowledge of vector databases and RAG implementations
  • Understanding of message queues and event-driven architectures
  • Background in enterprise SaaS products
  • Track record of mentoring other engineers

What the JD emphasized

  • secure LLM deployments
  • RAG implementations

Other signals

  • building internal GenAI platform
  • powering AI-driven solutions
  • RAG-based knowledge retrieval
  • complex agentic workflows
  • secure LLM deployments