Senior Solution Architect - Data, Ontology & AI Platforms
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Key skills for this role
About the Role
Blackford Technologies is seeking a Senior Solution Architect to own the technical vision and lead an engineering team building a modern data, semantic, and AI platform.
Key Skills for This Role
Responsibilities
- Own the end to end architecture of the data semantic AI stack and the contracts between layers (GraphQL, REST, protobuf, event streams, tool calling interfaces)
- Lead and mentor the engineering team, reviewing designs and PRs against shared architecture decisions and reference patterns
- Architect the data platform — batch ingestion, a versioned lakehouse with branch based safety, distributed transformation and orchestration, dataset versioning, and event driven pipelines
- Design the semantic/ontology layer — entity, property, and relationship modeling; semantic query APIs; fine grained policy enforcement; and dataset/column linkage that gives data meaning
- Drive the AI & MLOps layer — agent orchestration, AI copilot experiences, model guardrails, LLM inference, and the full experiment tracking model registry serving lifecycle
- Guarantee data and semantic quality — quality gates, ontology consistency, embedding/vector strategy, PII handling, and behavior evaluation that gates releases
- Embed governance by design — data lineage, auditability, multi tenancy, and policy enforcement as first class concerns
- Be the technical voice with stakeholders, translating data governance and AI operations requirements into architecture, and running design reviews and demos
Requirements
- 10+ years in software/data/ML engineering, with 5+ years as a solution, data, or platform architect leading teams and owning system level design
- Deep distributed data expertise — lakehouse architecture (e.g. Iceberg, Delta, Hudi), data versioning/branching, distributed compute (e.g. Spark), event driven pipelines (e.g. Kafka), and data lineage/quality patterns
- Semantic / ontology / knowledge modeling experience — entity relationship or ontology design, semantic query layers, and GraphQL APIs; familiarity with policy as code for fine grained access control
- MLOps and AI agent systems — model registry and serving (e.g. MLflow, KServe), feature stores, LLM inference (e.g. vLLM), agent frameworks (e.g. LangGraph), and guardrails/safety patterns
- Vector search & retrieval — embeddings, semantic search (e.g. Qdrant, pgvector), and RAG patterns
- API and contract design — GraphQL, REST, protobuf, and event schemas with strong versioning discipline
- Hands on coding in Python across the data/ML stack (e.g. FastAPI, PySpark)
- Strong technical leadership — mentoring engineers, writing architecture decision records, running design and PR reviews, and aligning a team around shared patterns
- Governance by design instinct — lineage, audit, multi tenancy, and policy treated as first class, not afterthoughts
Full Job Posting
Overview
- We are looking for a Senior Solution Architect to own the technical vision and lead an engineering team building a modern data, semantic, and AI platform. You will be the technical authority for how data flows, gains semantic structure, trains models, and powers AI agents — leading a multidisciplina
Key Responsibilities
- Own the end to end architecture of the data semantic AI stack and the contracts between layers (GraphQL, REST, protobuf, event streams, tool calling interfaces).
- Lead and mentor the engineering team, reviewing designs and PRs against shared architecture decisions and reference patterns.
- Architect the data platform — batch ingestion, a versioned lakehouse with branch based safety (staging quality gates promotion), distributed transformation and orchestration, dataset versioning, and event driven pipelines.
- Design the semantic/ontology layer — entity, property, and relationship modeling; semantic query APIs; fine grained policy enforcement; and dataset/column linkage that gives data meaning.
- Drive the AI & MLOps layer — agent orchestration, AI copilot experiences, model guardrails, LLM inference, and the full experiment tracking model registry serving lifecycle.
- Guarantee data and semantic quality — quality gates, ontology consistency, embedding/vector strategy, PII handling, and behavior evaluation that gates releases.
- Embed governance by design — data lineage, auditability, multi tenancy, and policy enforcement as first class concerns.
- Be the technical voice with stakeholders, translating data governance and AI operations requirements into architecture, and running design reviews and demos.
Must Have Qualifications
- 10+ years in software/data/ML engineering, with 5+ years as a solution, data, or platform architect leading teams and owning system level design.
- Deep distributed data expertise — lakehouse architecture (e.g. Iceberg, Delta, Hudi), data versioning/branching, distributed compute (e.g. Spark), event driven pipelines (e.g. Kafka), and data lineage/quality patterns.
- Semantic / ontology / knowledge modeling experience — entity relationship or ontology design, semantic query layers, and GraphQL APIs; familiarity with policy as code for fine grained access control.
- MLOps and AI agent systems — model registry and serving (e.g. MLflow, KServe), feature stores, LLM inference (e.g. vLLM), agent frameworks (e.g. LangGraph), and guardrails/safety patterns.
- Vector search & retrieval — embeddings, semantic search (e.g. Qdrant, pgvector), and RAG patterns.
- API and contract design — GraphQL, REST, protobuf, and event schemas with strong versioning discipline.
- Hands on coding in Python across the data/ML stack (e.g. FastAPI, PySpark).
- Strong technical leadership — mentoring engineers, writing architecture decision records, running design and PR reviews, and aligning a team around shared patterns.
- Governance by design instinct — lineage, audit, multi tenancy, and policy treated as first class, not afterthoughts.
Nice to Have
- Experience in regulated industries (financial services, healthcare, government) where data governance, lineage, and auditability are requirements.
- Familiarity with tool calling / agent integration protocols and contracts.
- Background with declarative transformation frameworks (e.g. dbt) and data quality tooling.
- Working comfort with Kubernetes as a deployment substrate — enough to be effective, even if it is not your primary focus.
- Familiarity with an observability stack (e.g. OpenTelemetry, Prometheus, Grafana) and immutable audit patterns.
- Prior experience taking a data/AI platform from proof of concept to production end to end.
Benefits
- Data flows cleanly from source versioned dataset semantic layer intelligent action, with quality and policy gates holding under real workloads.
- The semantic layer becomes the trusted foundation that agents, copilots, and analysts query with confidence.
- ML experiments, models, and agents ship predictably and safely, with lineage and audit intact.
- Architecture boundaries and platform invariants hold as the system scales.
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