Enterprise Architect

**Company Overview**:
Milestone Technologies is a global IT managed services firm that partners with organizations to scale their technology, infrastructure and services to drive specific business outcomes such as digital transformation, innovation, and operational agility. Milestone is focused on building an employee-first, performance-based culture and for over 25 years, we have a demonstrated history of supporting category-defining enterprise clients that are growing ahead of the market. The company specializes in providing solutions across Application Services and Consulting, Digital Product Engineering, Digital Workplace Services, Private Cloud Services, AI/Automation, and ServiceNow. Milestone culture is built to provide a collaborative, inclusive environment that supports employees and empowers them to reach their full potential.

**Job Overview**:
**Job Overview**:
We’re hiring an **Enterprise Architect** to define and govern the technical blueprint for an AI-first, data-centric platform. The role spans target architecture, reference patterns, and guardrails across orchestration, knowledge systems, retrieval pipelines, security, and operations. You’ll partner with product, security, data, and engineering leads to translate business outcomes into resilient, scalable designs-then guide teams to deliver them with measurable quality, cost, and reliability.

**Key Responsibilities**:

- ** Target Architecture & Roadmap**: Define the end-to-end architecture (experience, orchestration, agents/tools, data/knowledge, security, observability) and a phased roadmap.
- ** Reference Patterns & Standards**: Establish patterns for multi-agent orchestration, connector/adapter design, model routing, policy-as-code, and evaluation/observability; produce reference implementations.
- ** Knowledge & Retrieval**: Architect the coexistence of **knowledge graph** reasoning and **retrieval (RAG)** baselines; select graph/embedding stores, query patterns, and caching strategies.
- ** Integration & Platform Services**: Define typed interfaces and contracts for AI engines, vector/graph stores, SQL/warehouse, and external tools; standardize retries, idempotency, and failure isolation.
- ** Security & Governance**: Embed SSO/IDP, secret management, entitlement models (role/row/column), data minimization, auditability, and red-team controls into the design.
- ** Evaluation & SLOs**: Institutionalize quality gates (faithfulness/correctness, precision/recall, latency), automated re-evaluation on change, and service SLOs with error budgets.
- ** Operability**: Drive choices for tracing/logging/metrics, incident response, capacity planning, and cost/performance budgets; ensure CI/CD, IaC, and environment promotion gates.
- ** Technical Leadership**: Review designs/PRs, mentor leads, resolve cross-team trade-offs, and communicate architecture decisions to technical and non-technical stakeholders.

**Required Skills**:

- ** Architecture Mastery**: 3-5+ years designing distributed systems or platforms (microservices, event-driven, API-first) on cloud environments.
- ** AI/LLM Platform Patterns**: Practical experience with multi-agent orchestration, model/embedding selection, **RAG** pipelines, and **knowledge graph** solutions.
- ** Data & Knowledge Stores**: Hands-on with one or more: graph DBs (RDF/SPARQL or property graph/Gremlin), vector DBs (pgvector, Milvus, Pinecone, Weaviate), SQL/warehouse.
- ** Security-by-Design**: SSO/OAuth/OIDC, secrets management, least-privilege design, policy-as-code, audit logging, and data privacy controls.
- ** Interfaces & Contracts**: Strong API design (REST/gRPC), typed schemas (OpenAPI/JSON Schema), error taxonomies, idempotency, and backoff strategies.
- ** Observability & Reliability**: OpenTelemetry (traces/metrics/logs), call graphs, SLOs/error budgets, resiliency patterns (circuit breakers, bulkheads, retries, backpressure).
- ** DevOps & Delivery**: IaC (Terraform/CloudFormation), CI/CD, artifact/version management, environment promotion and change control.
- ** Communication & Leadership**: Clear decision records, stakeholder alignment, and the ability to mentor senior engineers and influence across teams.

**Good to Have Skills**:

- ** Graph & Query Expertise**: SPARQL, SHACL, Gremlin/PGQL; ontology/taxonomy stewardship and data lineage/provenance.
- ** Evaluation Tooling**: Experience with LLM evaluation harnesses (e.g., custom scoring frameworks, judge models, A/B test rigs), regression dashboards, and automated re-eval pipelines.
- ** Model & Tooling Ecosystem**: Familiarity with OpenAI, open-weights (Llama/Mistral), and connector protocols (e.g., MCP-style adapters).
- ** Runtime & Infra**: Service mesh (Istio/Linkerd), API gateways, Kafka/event streaming, feature flags, multi-tenancy, and cost governance for model/runtime spend.
- ** Security & Compliance**: Exposure to SOC 2/ISO 27001 (or comparable) practices; prompt-injection mitigation and data-exfiltration controls for LLMs.
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