Architect

AI Architect

Key Responsibilities

• AI Architecture & Patterns:

  Define reference architectures for LLM-powered systems (planner/reasoner, router/composer, tool/connector mesh), including typed I/O contracts and failure isolation.

• Knowledge & Retrieval Strategy:

  Architect coexistence of

  Knowledge Graph

  reasoning and

  RAG

  baselines; select embedding/vector stores, chunking, retrieval/reranking, and subgraph/query patterns.

• Model Strategy & Routing:

  Establish model selection policies (small→large escalation), prompt/adapter patterns, caching, and cost/latency budgets; document routing outcomes.

• Evaluation & Quality Gates:

  Design test sets and scoring rubrics (faithfulness/correctness, precision/recall, multi-hop coverage, latency); implement automated re-evaluation on any change (model/agent/prompt/data).

• Safety & Guardrails:

  Specify policy-as-code, entitlement checks (role/row/column), PII/PHI handling, and content moderation; define red-team tests and jailbreak defenses.

• Data & Interfaces:

  Define schemas for AI inputs/outputs; guide ontology/taxonomy alignment; ensure provenance/lineage for KG/RAG pipelines; minimize data movement.

• Operability & Observability:

  Standardize tracing/logging/metrics for AI runs (call graphs, token/latency/cost); set SLOs and error budgets; partner with DevOps for CI/CD and environment promotion gates.

• Technical Leadership:

  Review designs/PRs; mentor AI engineers; communicate decisions/trade-offs to stakeholders; maintain decision records and roadmaps.

Required Skills

• Applied LLM Systems:

  1+ years in ML/AI or platform architecture with production LLM solutions (planning/reasoning, tool use, function-calling, agent ecosystems).

• Knowledge & Retrieval:

  Hands-on with

  Knowledge Graphs

  (RDF/SPARQL or property graph/Gremlin) and

  RAG

  (chunking, embeddings, retrieval/reranking); practical trade-offs between the two.

• Model & Vector Ecosystem:

  Experience with at least one major model platform (Azure OpenAI, Vertex AI, Anthropic, open-weights) and vector DBs (pgvector, Pinecone, Weaviate, Milvus) plus search (OpenSearch/Elasticsearch).

• Evaluation Engineering:

  Ability to construct evaluation harnesses, design rubrics, and automate regression testing; familiarity with A/B testing and human-in-the-loop review.

• Security-by-Design:

  SSO/OIDC, secrets management, least-privilege design, policy-as-code, data minimization, and auditability for AI systems.

• Software & APIs:

  Strong API design (REST/gRPC), JSON schema contracts, error taxonomies, retries/backoff/idempotency; proficiency in one or more languages (Python, TypeScript/Node.js, Go, or Java).

• Observability & Reliability:

  OpenTelemetry or equivalent for traces/metrics/logs; resiliency patterns (circuit breakers, bulkheads, backpressure); performance tuning and cost governance.

Good to Have Skills

• Ontology & Graph Practices:

  SHACL, OWL, ontology stewardship, and data quality checks; graph query optimization and subgraph extraction patterns.

• Prompt & Tooling Ops:

  Prompt versioning, prompt injection defenses, retrieval parameter tuning, and structured tool-use schemas (e.g., MCP-style adapters).

• MLOps & Platforms:

  IaC (Terraform), CI/CD for models/prompts/configs, feature flags, canary releases; experience with GPU/accelerator considerations.

• External Tools:

  Designing safe, auditable action patterns and HITL approvals for external tool execution (simulation/models, document generation, ticketing).

• Cost/Performance Analytics:

  Token accounting, cache strategies, and per-agent cost ceilings; dashboards for cost-per-answer and latency P50/P95 targets.

• UX for Explainability:

  Collaborating on rationale/explanation UX so users understand sources, subgraphs, and model/tool decisions.