AI Architect

Job Role- Data and AI Architect

Location - London, UK.

Number of Position-1

Job Description:

Architecture & Solution Design

• Define 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 & Tooling

• Architect agent systems (single/multi-agent) including:
• Task decomposition, planners/executors, reflection/verification loops
• Tool use patterns (APIs, databases, search, workflow engines)
• Guardrails to prevent unsafe tool actions and hallucinated commands
• Build reliable flows for “human-in-the-loop” decision points and approvals (e.g., procurement, customer comms, incident triage).

3) Retrieval, Knowledge Systems & Data Design

• Lead design of knowledge ingestion pipelines:
• document parsing, chunking strategies, embeddings, metadata, lineage, freshness SLAs
• Architect vector search and hybrid retrieval:
• semantic + keyword, reranking, filtering, ACL-aware retrieval
• Ensure retrieval respects access control, PII handling, data residency, and auditability.

4) Production Engineering, Reliability & Cost

• Set non-functional requirements for GenAI workloads:
• SLOs, latency budgets, fallback models, caching, rate limiting
• Design 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 AI

• Establish AI security posture:
• prompt injection defenses, data exfiltration controls, tool sandboxing
• Define policies and controls for:
• sensitive data, logging, redaction, encryption, secret management, and auditing
• Collaborate with risk/compliance to drive:
• model governance, content safety, bias/quality monitoring, and regulatory alignment

6) Evaluation, Observability & Continuous Improvement

• Create evaluation frameworks:
• offline evals (golden sets), automated regression, and scenario-based testing
• Instrument systems for observability:
• traces, prompt/versioning, retrieval diagnostics, tool-call logs, and outcome metrics
• Run A/B tests and iterate on prompts, retrieval, and agent policies based on measurable outcomes.

7) Leadership & Stakeholder Management

• Partner 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 & Experience

Core 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 patterns
• RAG expertise:
• embeddings, vector DBs, hybrid retrieval, reranking, chunking strategies, evaluation
• Cloud architecture (Azure/AWS/GCP) with production engineering rigor:

Security & Governance (Must Have)

• Understanding of GenAI-specific threats:
• prompt injection, data leakage, jailbreaks, insecure tool calling
• Familiarity with enterprise controls:
• IAM, key management, encryption, network isolation, audit logging
• Responsible AI practices:
• evaluation, content moderation, privacy, and compliance-by-design

Architecture & Systems Skills (Must Have)

• Distributed system design:
• scalability, fault tolerance, caching, performance tuning
• Observability:
• logging/metrics/tracing, prompt/version tracking, monitoring SLIs/SLOs
• Cost management and performance optimization:
• model selection/routing, token reduction, caching, batching

Preferred / Nice-to-Have Skills

• Fine-tuning approaches:
• LoRA/QLoRA, instruction tuning, adapters, distillation (when appropriate)
• Experience with:
• Knowledge graphs, semantic layers, enterprise search
• Advanced evaluation:
• LLM-as-judge with safeguards, rubric scoring, adversarial testing
• MLOps/LLMOps toolchains:
• experiment tracking, feature stores, model registries, data quality tools
• Domain experience:
• customer support automation, developer productivity copilots, IT ops agents, finance or healthcare compliance
• Experience building platforms:
• reusable agent frameworks, reusable RAG components, multi-team enablement