Description

Responsibilities

• Define secure application architectures covering web, mobile, APIs, AI services, data stores, models, and enterprise integrations.
• Identify and mitigate traditional application vulnerabilities as well as AI-specific risks such as prompt injection, data leakage, RAG poisoning, model misuse, and excessive agent privileges.
• Design secure RAG pipelines with proper document validation, access control, retrieval security, vector-store protection, and response validation.
• Evaluate and select LLMs, embedding models, rerankers, vector databases, and graph technologies based on security, quality, scale, latency, and cost.
• Compare and design solutions using platforms such as Neo4j, Pinecone, Weaviate, Milvus, Azure AI Search, Chroma, pgvector, or similar technologies.
• Define production deployment, MLOps/LLMOps, model monitoring, evaluation, drift detection, retraining, versioning, and rollback strategies.
• Establish Responsible AI, model governance, explainability, auditability, compliance, and human oversight.
• Stay aware of emerging AI security threats and industry incidents and translate those learnings into practical architecture and security controls.

Key Skills

• Application, API, mobile, and on-premises security.
• AI security, threat modelling, prompt-injection protection, and secure AI design.
• LLMs, RAG, embeddings, semantic search, hybrid search, GraphRAG, and AI agents.
• Vector and graph databases, including selection and architecture trade-offs.
• Data architecture, data governance, privacy, PII protection, lineage, and access control.
• MLOps/LLMOps, model deployment, model registry, monitoring, drift detection, and evaluation.
• Model selection, benchmarking, hallucination/groundedness evaluation, and production quality controls.
• IAM, RBAC/ABAC, encryption, secrets management, network isolation, and secure SDLC.
• Responsible AI, model governance, bias, explainability, compliance, and auditability.
• Kubernetes/OpenShift, containers, GPUs, model serving, and enterprise observability.
• Strong communication, technical leadership, and architecture decision-making.

Experience

• 10+ years of software, architecture, data, or AI engineering experience.
• Experience designing enterprise-grade AI solutions, preferably in regulated, government, banking, telecom, or other high-security environments.
• Experience taking AI solutions from data ingestion and model selection through deployment, monitoring, governance, and production operations

Key Skills

Education

Bachelor's degree