AI Leader

What is the project, and why should you care?Our client is seeking a compact, senior engineering squad from a single vendor to build and evolve an AI‑powered, security‑first platform. The team will deliver new features, integrations, and a full English↔Hebrew (LTR↔RTL) experience. Tech stack: Frontend – Angular; Backend – .NET (C#) microservices; Cloud – Azure; CI/CD – Azure DevOps; Design – Figma; AI – Azure OpenAI / OSS models (as applicable), vector search (e.g., Azure AI Search/pgvector), orchestration (e.g., Semantic Kernel/LangChain), MCP for tool integrations.You will be an excellent fit for this position if you have:Tech Stack & Core Expertise:Backend: C#, .NET, Microservices architectureCloud & DevOps: Azure, AKS (Kubernetes), Azure DevOps CI/CD, Infrastructure as CodeAI/ML Development:Experience building and deploying offline models (non-SaaS / tenant-isolated AI instances)Strong background in RAG (Retrieval-Augmented Generation) pipelines, embedding creation, and index managementExpertise in classification models, fine-tuning, and AI lifecycle automationHands-on experience with MLflow, model versioning, and training pipelinesFamiliarity with vector databases, document ingestion, and data preprocessing for unstructured dataExperience integrating AI models with microservices and APIs in production environmentsSecurity & Compliance:Strong understanding of application security, data protection, and secure AI design principlesExperience implementing role-based access, data isolation, and compliance frameworks (e.g., NIST, ISO, GDPR)Preferred Soft Skills:Ability to lead cross-functional development between AI, backend, and DevOps teamsComfortable defining architecture, reviewing code, and mentoring engineersStrong documentation and communication skillsHere are some of the things you’ll be working on:Own the AI architecture and main components; lead the team; drive security, quality, and delivery.Define AI system architecture: model serving, retrieval (RAG), evaluation, guardrails, and data pipelines.Choose/operate model endpoints (Azure OpenAI or OSS), vector DB, caching, and orchestration framework.Establish AI safety (prompt‑injection defenses, PII redaction, rate‑limit/abuse protection, content filters).Lead MLOps (datasets, fine‑tuning/LoRA when needed, evals, versioning, rollback, drift monitoring).Own cross‑team engineering standards (coding, testing, docs, ADRs) and delivery plan.