As Lead Security Engineer, you will design and optimize large-scale AI/ML platforms and LLM-powered applications.

Key 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 Lang Chain or Llama Index.

• 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.

Required Skills

• 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 (Py Torch, Tensor Flow, JAX).

• Strong understanding of transformer architectures, LLMs, and Hugging Face ecosystem.

• Hands-on experience with frameworks and libraries including Tensor Flow, Py Torch, 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.

Preferred Skills

• 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.