Generative AI Engineer

Architecture & Solution DesignDefine reference architectures for GenAI systems: RAG, agentic orchestration, tool/function calling, multi-step reasoning workflows, memory patterns, and context strategies.Design multi-tenant and enterprise-scale GenAI platforms with clear separation of concerns: UI, orchestration, retrieval, inference, evaluation, and observability.Select model strategies: hosted LLMs, open-weight models, fine-tuning vs. prompt/RAG, latency and cost tradeoffs, and deployment patterns.2) Agentic AI Orchestration & ToolingArchitect agent systems (single/multi-agent) including: Task decomposition, planners/executors, reflection/verification loopsTool use patterns (APIs, databases, search, workflow engines)Guardrails to prevent unsafe tool actions and hallucinated commandsBuild reliable flows for "human-in-the-loop" decision points and approvals (e.G., procurement, customer comms, incident triage).3) Retrieval, Knowledge Systems & Data DesignLead design of knowledge ingestion pipelines: document parsing, chunking strategies, embeddings, metadata, lineage, freshness SLAsArchitect vector search and hybrid retrieval: semantic + keyword, reranking, filtering, ACL-aware retrievalEnsure retrieval respects access control, PII handling, data residency, and auditability.4) Production Engineering, Reliability & CostSet non-functional requirements for GenAI workloads: SLOs, latency budgets, fallback models, caching, rate limitingDesign cost controls: prompt/token optimization, model routing, batching, and usage governance.Implement resiliency patterns: circuit breakers, retries, queue-based orchestration, idempotency.5) Security, Risk & Responsible AIEstablish AI security posture: prompt injection defenses, data exfiltration controls, tool sandboxingDefine policies and controls for: sensitive data, logging, redaction, encryption, secret management, and auditingCollaborate with risk/compliance to drive: model governance, content safety, bias/quality monitoring, and, Senior Agentic & Generative AI Consultant - Full Time - Hybrid ( 3 days Office, 2 days WFH) London, United Kingdom (Hybrid) Full Time (10 - 15 Years Exp) Role Overview We are seeking a Senior Agentic & Generative AI Consultant who is a recognized thought leader and...

regulatory alignment6) Evaluation, Observability & Continuous ImprovementCreate evaluation frameworks: offline evals (golden sets), automated regression, and scenario-based testingInstrument systems for observability: traces, prompt/versioning, retrieval diagnostics, tool-call logs, and outcome metricsRun A/B tests and iterate on prompts, retrieval, and agent policies based on measurable outcomes.7) Leadership & Stakeholder ManagementPartner with product leaders to identify high-value use cases and define roadmap.Mentor engineers and data scientists on best practices for LLM apps.Produce architecture artifacts: ADRs, threat models, system diagrams, runbooks.Required Skills & ExperienceCore Technical Skills (Must Have)8+ years in software/solution architecture with 2+ years delivering GenAI/LLM solutions in production (adjust as needed).Strong knowledge of LLMs: prompting patterns, context windows, tool/function calling, model limitations, and safety risks.Agentic AI design experience: orchestrators, workflows, multi-step reasoning, tool usage, HITL patternsRAG expertise: embeddings, vector DBs, hybrid retrieval, reranking, chunking strategies, evaluationCloud architecture (Azure/AWS/GCP) with production engineering rigor: microservices, containers (Docker/K8s), serverless, CI/CDSolid programming skills (one or more): Python, TypeScript/JavaScript, Java, C#Experience with APIs and integration patterns: REST/gRPC, event-driven systems, queues, workflow enginesSecurity & Governance (Must Have)Understanding of GenAI-specific threats: prompt injection, data leakage, jailbreaks, insecure tool callingFamiliarity with enterprise controls: IAM, key management, encryption, network isolation, audit loggingResponsible AI practices: evaluation, content moderation, privacy, and compliance-by-designArchitecture & Systems Skills (Must Have)Distributed system design: scalability, fault tolerance, caching, performance tuningObservability: logging/metrics/tracing, prompt/version tracking, monitoring SLIs/SLOsCost management and performance optimization: model selection/routing, token reduction, caching, batchingPreferred / Nice-to-Have SkillsFine-tuning approaches: LoRA/QLoRA, instruction tuning, adapters, distillation (when appropriate)Experience with: Knowledge graphs, semantic layers, enterprise searchAdvanced evaluation: LLM-as-judge with safeguards, rubric scoring, adversarial testingMLOps/LLMOps toolchains: experiment tracking, feature stores, model registries, data quality toolsDomain experience: customer support automation, developer productivity copilots, IT ops agents, finance or healthcare complianceExperience building platforms: reusable agent frameworks, reusable RAG components, multi-team enablement Similar jobs

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