Lead Security Engineer

JPMorganChase • Plano, TX

2d

About The Position

As Lead Security Engineer, you will design and optimize large-scale AI/ML platforms and LLM-powered applications. You will lead the architecture of end-to-end solutions, integrate advanced cybersecurity measures, and collaborate with teams to deliver high-impact AI capabilities for enterprise environments.

Requirements

• Formal Training or certification with 5+ years of experience in high-impact AI capabilities for enterprise environments.
• Advanced proficiency in Python and deep learning frameworks (PyTorch, TensorFlow, JAX).
• Strong understanding of transformer architectures, LLMs, and Hugging Face ecosystem.
• Hands-on experience with frameworks and libraries including TensorFlow, PyTorch, BERT/LLMs, Hugging Face, OpenCV, scikit-learn, SKLearn, Pandas, Flask, and React.
• Experience with cloud-based ML platforms (AWS Sage Maker, Google Vertex AI, Azure ML), containerization (Docker), and orchestration (Kubernetes).
• Hands-on experience designing and deploying RAG systems using Lang Chain, Llama Index, Pinecone, or Faiss.
• Expertise in secure model deployment, access control, and data governance.
• Excellent leadership, communication, and collaboration skills.

Nice To Haves

• Experience with multi-modal AI integration and advanced optimization techniques.
• Familiarity with CI/CD pipelines, automation tools, and frontend frameworks.
• Certifications in AI/ML, cloud platforms, Kubernetes, or cybersecurity.
• Advanced degree (master’s or PhD) in Computer Science, AI, Data Science, or related field.
• Exposure to regulated industries and compliance frameworks.

Responsibilities

• Architect and deploy state-of-the-art LLM architectures (e.g., GPT, LLaMA, Mixtral) using techniques like LoRA and RLHF for domain-specific tasks.
• Develop advanced prompt engineering strategies and orchestrate LLM-powered applications using frameworks like LangChain or LlamaIndex.
• Design and manage data pipelines for collection, cleaning, and preparation of high-quality datasets.
• Implement Retrieval-Augmented Generation (RAG) systems, managing vector databases and embedding models.
• Build and maintain scalable, secure inference pipelines while continuously monitoring for model drift.
• Apply optimization techniques such as quantization and pruning to improve model efficiency.
• Ensure all AI solutions meet cybersecurity standards and compliance requirements.
• Stay current with advancements in NLP, transformer architectures, and generative AI research.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed
• Number of Employees: 5,001-10,000 employees