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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Artificial Intelligence Specialist - **Company:** HCLTech - **Location:** Greater London, UK - **Experience:** Experienced - **Contract:** Permanent contract - **Skills:** A/B Testing, Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Audit Trail, Microsoft Azure, Cloud Engineering, Databases, Data Architecture, Fault Tolerance, Identity and Access Management, Key Management, Knowledge-Based Systems, Metadata, Performance Tuning, Search Technologies, Management of Software Versions, Data Logging, Data Ingestion, Large Language Models, Multi-Agent Systems, Caching, Rate Limiting, Event Driven Architecture, Machine Learning Operations, Virtual Agents, Software Coding, Grpc - **Published:** August 26, 2026 - **Apply:** https://www.collegerecruiter.com/job/2815130656-artificial-intelligence-specialist ## About the Role * 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. * orchestrators, workflows, multi-step reasoning, tool usage, HITL patterns * RAG expertise: * Cloud architecture (Azure/AWS/GCP) with production engineering rigor: * Solid programming skills (one or more): * Experience with APIs and integration patterns: * REST/gRPC, event-driven systems, queues, workflow engines Security & Governance (Must Have) * Understanding of GenAI-specific threats: * 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) * 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 ## Description 1) Define reference architectures for GenAI systems: RAG, agentic orchestration, tool/function calling, multi-step reasoning workflows, memory patterns, and context strategies. * Define reference architectures for GenAI systems including 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: * 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. * Establish AI security posture: * 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 * 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. * orchestrators, workflows, multi-step reasoning, tool usage, HITL patterns * RAG expertise: * Cloud architecture (Azure/AWS/GCP) with production engineering rigor: * Solid programming skills (one or more): * Experience with APIs and integration patterns: * REST/gRPC, event-driven systems, queues, workflow engines Security & Governance (Must Have) * Understanding of GenAI-specific threats: * 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) * 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: * Advanced evaluation: * LLM-as-judge with safeguards, rubric scoring, adversarial testing * MLOps/LLMOps toolchains: * customer support automation, developer productivity copilots, IT ops agents, finance or healthcare compliance * Experience building platforms ## Related Videos - [Exploring the Power of gRPC-Gateway for Writing RESTful Services](https://www.wearedevelopers.com/videos/2072-exploring-the-power-of-grpc-gateway-for-writing-restful-services) - [HTTP headers that make your website go faster](https://www.wearedevelopers.com/videos/1676-http-headers-that-make-your-website-go-faster) - [A Data Mesh needs Open Metadata](https://www.wearedevelopers.com/videos/505-a-data-mesh-needs-open-metadata) - [How to Avoid LLM Pitfalls - Mete Atamel and Guillaume Laforge](https://www.wearedevelopers.com/videos/1328-how-to-avoid-llm-pitfalls-mete-atamel-and-guillaume-laforge) - [Boosting OpenSearch Performance: gRPC Search in Action](https://www.wearedevelopers.com/videos/1964-boosting-opensearch-performance-grpc-search-in-action) - [Make it simple, using generative AI to accelerate learning](https://www.wearedevelopers.com/videos/969-make-it-simple-using-generative-ai-to-accelerate-learning) ## Related Articles - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [Got AI ideas but no money? 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