Must-have:

• Experience designing multi-step workflows
• Experience developing agent-based systems: single-agent and/or multi-agent
• Orchestration: planning, execution, retry, fallback, verification
• Management of:
• State
• Context
• Memory
• Understanding when an agentic approach is needed and when it's not
• Understanding of trust boundaries in agentic systems
• Principle of least privilege for tool permissions
• Protection against indirect prompt injection via external data (retrieval, tool results, external APIs)

1.3. Evaluation & Quality Control

Must-have:

• Building evaluation pipelines
• Offline evaluation
• Comparing prompt / model versions
• Quality metrics
• Approaches to online validation / A/B testing / human review loops
• Ability to connect evaluation to real product quality

1.4. Context Management & Hallucination Control

Must-have:

• Context management:
• Chunking strategies
• Context window optimization
• Memory patterns
• Retrieval scope control
• Hallucination reduction techniques:
• Grounding
• Retrieval
• Tool-based verification
• Constraints
• Self-check / validation patterns

1.5. Production / LLM Ops / Reliability

Must-have:

• Retries / exponential backoff
• Timeout handling
• Fallbacks / model routing
• Degraded mode / graceful failure
• Rate-limit handling
• Observability:
• Latency
• Token usage
• Cost
• Failure rate
• Output quality signals
• Cost control
• Monitoring and debugging AI systems in production
• PII handling: filtering before logging, tenant isolation in memory and retrieval
• Output validation and content guardrails
• Awareness of data residency risks when using external LLM APIs

1.6. Data / Retrieval

Must-have:

• Understanding of retrieval pipelines:
• Embeddings
• Chunking
• Reranking
• Retrieval quality tuning
• Experience with vector storage / vector DB of any type
• Working with structured and unstructured data

1.7. Engineering Foundation

Must-have:

Str