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:
Artificial Intelligence Specialist in London employer: HCL Technologies Limited
HCL Technologies Limited is an exceptional employer located in the vibrant City of London, offering a dynamic work culture that prioritises employee growth and development. With a strong focus on customer service excellence and operational efficiency, employees benefit from comprehensive training programs and opportunities to enhance their skills while contributing to meaningful projects. The collaborative environment fosters innovation and teamwork, making it an ideal place for those seeking a rewarding career in program and project management.
