About the Role:
CrowdStrike is looking for a Senior AI Cloud Engineer to join our growing Charlotte AI Engineering team in Bucharest. You will provide hands-on technical leadership while building globally distributed, fault-tolerant and highly scalable cloud services and agentic AI systems.
The role spans Charlotte AI and CrowdStrike’s broader Agentic SOC & SOAR products portfolio. You will help turn AI research and platform capabilities into production products that coordinate agents, data, automation and human expertise to accelerate security detection, investigation and response.
This is a deeply hands-on role. Demonstrated experience building agentic systems and agentic AI products, and deploying and operating them in production, is required. Prior experience with Go is helpful but not required; strong distributed-systems fundamentals and the ability to learn new technologies are essential.
CrowdStrike is a cybersecurity company, but we do not require candidates for this role to have prior security industry experience. We will mentor and train in security topics as needed. We do expect a strong interest in CrowdStrike's mission and a willingness to engage with the needs of our product teams and customers.
This is a hybrid position based in Bucharest, Romania. Candidates should be comfortable working from the Bucharest office twice a week.
About the Product:
CrowdStrike has pioneered the use of artificial intelligence since we first introduced AI-powered protection to replace signature-based antivirus over 10 years ago, and we’ve continued to deeply integrate AI across our platform.
Using recent advances in Large Language Model technologies, CrowdStrike introduced Charlotte AI, a generative AI security analyst. It uses high-fidelity security data and is seamlessly integrated with CrowdStrike’s industry-leading threat hunters, managed detection and response operators, and incident response experts. The Charlotte AI engine is a first-of-its-kind offering that empowers users of all skill levels to leverage the data available in the Falcon platform while reducing security operations complexity and cost.
Charlotte AI also helps power CrowdStrike’s Agentic SOC vision, which combines unified Falcon data, specialized agents, automation and human-in-command workflows to accelerate security operations. This role will contribute to product and platform capabilities across Charlotte AI and the broader Agentic SOC.
Learn more about Charlotte AI, Agentic SOC Transformation and the public announcement of Charlotte Agentic SOAR.
What You'll Do:
- Provide hands-on technical leadership for the architecture, development and operation of backend microservices and AI-native platform capabilities.
- Build, deploy and scale production agents and agentic systems, including orchestration, tool use, planning, retrieval, memory and human-in-the-loop workflows.
- Design reliable integrations with hosted LLMs and help evolve support for open-source and fine-tuned models, including model routing, fallbacks and graceful degradation.
- Define and improve evaluation strategies for agent quality, safety and reliability using offline and online evaluations, regression suites, production feedback and red-team testing.
- Work with platform and ML specialists to scale model serving and inference workloads, with a focus on availability, throughput, latency, capacity and cost.
- Build and maintain data and MLOps pipelines for data curation, evaluation, model adaptation and production deployment.
- Design safeguards for prompt injection, data leakage, unsafe tool use and cross-tenant access while maintaining auditability and least-privilege access.
- Establish production-readiness standards, including SLOs, observability, tracing, testing, release controls, incident response and operational reviews.
- Use established CrowdStrike tools and services to build cloud solutions that help detect and counter targeted cyberattacks.
- Set technical direction, lead design reviews and make pragmatic architecture decisions across teams while continuing to write and review production code.
- Collaborate with engineering, product, data science, security research and operations teams to define and deliver solutions.
- Mentor engineers across a range of technologies and learn from them in return.
- Continuously improve product architecture, knowledge models, user experience, performance and reliability.
- Own your work end to end: develop, test, deploy, monitor and continuously improve your changes.
- Thrive in a collaborative and agile environment that values trust, learning and customer outcomes.
What You'll Need:
- A degree in Computer Science or a related field, or equivalent practical experience.
- Demonstrated hands-on experience building, deploying and operating LLM-powered agents or agentic systems in production.
- Significant software engineering experience, including technical leadership of complex, cross-team initiatives while remaining hands-on.
- Strong experience with backend microservices, cloud platforms, data-intensive systems and distributed-system concepts such as partitioning, concurrency, consistency, fault tolerance and horizontal scaling.
- Experience integrating language models into production software and designing for model and provider variability.
- Experience evaluating and troubleshooting non-deterministic AI systems, including quality, safety, latency, cost and reliability.
- Strong coding skills in Go, Python or a comparable language and a willingness to work across Go and Python services.
- A thorough understanding of engineering best practices, including appropriate testing strategies, effective peer review, secure design, resilient architecture and production observability.
- The ability to make sound technical trade-offs, communicate clearly and influence engineering direction across teams.
- The ability to thrive in a test-driven, collaborative and iterative engineering environment.
- A track record of meeting commitments and delivering high-quality software that is tested, reviewed, continuously integrated and operated in production.
- A team-first mindset and a commitment to mentoring and developing other engineers.
- Proven experience utilizing AI technologies to enhance decision-making, streamline workflows and processes, improve efficiency and drive business outcomes.
Tech Stack:
You do not need to know everything listed below; strong engineering fundamentals and the ability to learn are essential:
- Go (Golang)
- Python
- Docker
- Kubernetes
- AWS, Google Cloud Platform (GCP) and Oracle Cloud Infrastructure (OCI)
- Redis, S3, EC2 and IAM
- Kafka
- Git
- Cassandra
- Elasticsearch
- LLM APIs and open-source model ecosystems
- PyTorch and Hugging Face, or equivalent model-development ecosystems
- Agent orchestration, evaluation and observability tooling
- GPU-based inference and model-serving technologies such as vLLM, TGI, NVIDIA NIM or Triton
Bonus Points:
- Hands-on experience fine-tuning LLMs, including data curation, supervised fine-tuning and parameter-efficient techniques such as LoRA.
- Experience deploying and scaling open-source or fine-tuned LLMs in production, including GPU capacity planning, batching, caching, quantization and inference optimization.
- Experience with preference optimization, reinforcement learning from feedback or model distillation.
- Existing exposure to Go, AWS, Cassandra, Kafka and Elasticsearch.
- Prior experience in cybersecurity or intelligence.
- Contributions to relevant open-source projects, applied research or technical publications.
