Artificial Intelligence Specialist

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.

• 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).

• 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.

• 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

• 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.

• 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.

• 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
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• 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

• 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

• 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

• 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 tool chains:
• customer support automation, developer productivity copilots, IT ops agents, finance or healthcare compliance
• Experience building platforms: