CrowdStrike AI Security Engineer

Role

Technical AI Solution Engineer (part-time contract, 3–6 months, Plano, TX; senior/expert level; potential conversion to full-time). Lead design, architecture, development, and delivery of AI and Generative AI solutions across customer engagements, driving PoC/MVP creation and production deployments.

Key Responsibilities

• Lead end-to-end AI and Generative AI solution architecture, including system design, data flows, model selection, and integration patterns.
• Partner with customers, product managers, and business teams to identify high-impact AI-driven use cases and recommend solution approaches.
• Rapidly build proofs of concept (PoC) and MVPs using Vibe coding, LLMs, embeddings, retrieval, agents, and AI pipelines.
• Implement AI workflows such as MCP, RAG, fine-tuning, prompt engineering, agentic orchestration, and model optimization.
• Develop integrations with enterprise systems, APIs, data platforms, and cloud environments (AWS / Azure / Google Cloud Platform).
• Serve as subject-matter expert and advisor on modern AI, GenAI, LLM architectures, vector databases, and agentic automation.
• Conduct knowledge-sharing sessions, demos, and workshops (nice-to-have).

Requirements

• Strong hands-on experience with LLMs, Generative AI frameworks, and modern AI stacks (examples named: OpenAI, Azure OpenAI, Anthropic, Meta Llama).
• Experience building AI pipelines and architectures (MCP, RAG), multi-agent orchestration, prompt engineering and evaluation, and fine-tuning/parameter-efficient training (LoRA, adapters).
• Proficiency in Vibe coding and using AI code-generation tools.
• Proficiency in Python and/or Node.js.
• Experience with LangChain, Semantic Kernel, LlamaIndex and ML frameworks (PyTorch; TensorFlow optional).
• Experience with vector databases such as Pinecone, Weaviate, Chroma, Redis Vector.
• Knowledge of cloud AI services (Azure, AWS, Google Cloud Platform).
• Strong understanding of API architectures, microservices, DevOps practices, CI/CD, and containerization.
• Proven solution engineering and delivery skills: translating business needs to scalable AI designs, customer engagement, documentation, and architecture diagramming.
• Background: ~5–10+ years in software or solution engineering, with ~2–3+ years focused on AI/ML or GenAI implementation. Bachelor’s/Master’s in Computer Science, Engineering, AI/ML, or related field.

Preferred Qualifications

• Experience in telco/media or large enterprise environments.
• Knowledge of data engineering, ETL pipelines, feature stores.
• Experience with MLOps/LLMOps methodologies and tools, vector search optimization, knowledge graphs, or reinforcement learning.
• Familiarity with responsible AI, security, governance, and compliance frameworks.