Generative Content AI Engineer

You will design, build, and operationalize generative AI solutions that produce accurate, safe, and enterprise-grade content at scale. Your work will power LLM-driven applications, multimodal pipelines, and RAG systems deployed across Cehpoint's security-aware client ecosystem.

• Design and implement RAG pipelines combining LLMs with vector databases for domain-specific content generation and retrieval-augmented responses.
• Fine-tune and adapt open-source models (e.g., LLaMA, Mistral, Stable Diffusion) for specialized enterprise use cases using LoRA/QLoRA techniques.
• Build prompt engineering frameworks, guardrail systems, and output-validation layers to ensure factual accuracy, brand safety, and compliance.
• Deploy and containerize generative models into production using Docker, Kubernetes, and CI/CD pipelines with monitoring and A/B testing.
• Conduct model evaluation, benchmarking, and red-teaming to identify biases, hallucinations, and security vulnerabilities in generated content.
• Collaborate with the cybersecurity and product teams to integrate generative AI outputs into secure enterprise workflows and API services.

• Deep understanding of transformer architecture, attention mechanisms, and scaling laws underlying modern LLMs and diffusion models.
• Solid grasp of fine-tuning methodologies (full fine-tuning, parameter-efficient fine-tuning like LoRA/QLoRA), and when to use each approach.
• Foundational knowledge of information retrieval, semantic search, embedding models, and vector database indexing strategies.

• Proficiency in Python with hands-on experience using PyTorch, Hugging Face Transformers, and LangChain/LlamaIndex.
• Experience deploying models via FastAPI/Flask, Docker, and Kubernetes or equivalent orchestration platforms.
• Practical expertise with at least one major cloud platform (AWS SageMaker, GCP Vertex AI, or Azure ML) for training and inference.
• Hands-on work with vector databases such as Pinecone, Weaviate, Milvus, or FAISS, including embedding pipeline construction.
• Familiarity with evaluation frameworks (RAGAS, DeepEval, or custom metrics) and monitoring tools (Prometheus, Grafana, MLflow).

• Automatically experiments with cutting-edge open-source models and papers, prototyping new approaches before they reach production.
• Proactively investigates failure modes — hallucinations, prompt injections, adversarial jailbreaks — and designs mitigations.
• Stays current with the rapidly evolving generative AI landscape, contributing internal tech talks or proof-of-concept demos.

• Competitive compensation with annual performance bonuses and ESOP eligibility.
• Fully remote setup with a stipend for home office ergonomics and high-speed internet.
• Access to dedicated GPU compute credits for personal projects, experimentation, and paper reproduction.
• Direct mentorship from senior AI researchers and cybersecurity engineers across the organization.
• Continuous learning budget for conferences, certifications, and advanced courses in AI and security.