AI Engineer

Company Description PolyPhaze is a data intelligence platform that makes enterprise data AI-ready by unifying and governing information across systems. The PolyPhaze Knowledge Fabric™ connects disparate enterprise systems, resolves meaning, enforces referential integrity, and traces every insight back to its source. This ensures that human decision-makers and AI agents operate from the same trusted, compliant data foundation. The platform is model-agnostic, does not require disruptive rip-and-replace projects, and can be deployed in 30–90 days. PolyPhaze enables organizations to trust their data and act with confidence.
About the Role
PolyPhaze is looking for an AI Engineer who can do two things well: build production-grade AI solutions on top of the Knowledge Fabric and help clients understand what they are building and why it works.
This is not a research role, and it is not a prompt-engineering role. It is a software engineering role at the intersection of agentic AI, semantic data architecture, and enterprise systems. The work involves designing and implementing AI agents that read from and write back to governed data, configuring Knowledge Fabric connections and trust layers, governing agent behavior through code rather than through documentation, and working directly with clients whose stakes are real and whose tolerance for invisible failures is low.
The right person has built things in production, knows what breaks and why, and is fluent enough in the underlying architecture to explain it to a senior business leader and to a platform engineer on the same day.
What You Will Do
Design and Build AI Agents
· Design, implement, and deploy AI agents using MCP-compliant infrastructure, defining agent purpose, authority levels, MCP connection scope, and behavioral constraints as code.
· Implement the governance envelope for every agent you build; identity and provenance, authorization scope, authority level enforcement, audit hook, and behavioral constraints, baked into the architecture, not stated in a system prompt.
· Use multi-model routing to match tasks to the appropriate model tier, open-source, small language model, domain-specific, or frontier, and build cost governance logic that makes consumption visible before it becomes a problem.
· Use coding AI tools (Claude Code, ChatGPT Codex, Cursor, or equivalent) to accelerate agent development and use a second model to QA the implementation and generate end-user documentation from the same specification. Own the output.
Work with the Knowledge Fabric
· Connect source systems to the Knowledge Fabric using streaming, polled, or federated connection modes, selecting the appropriate mode for each system based on data residency, regulatory, and latency requirements.
· Configure entity resolution, semantic layer definitions, Trust Score thresholds, and freshness gates for the agents and use cases you are delivering.
· Implement and validate write-side governance: verification layer, lineage capture, agent identity attribution, and Trust Score propagation on every agent write.
· Debug Knowledge Fabric failures, distinguish entity resolution errors, semantic inconsistency failures, freshness gate violations, and write-side corruption from model or orchestration failures.
Deliver Client Solutions
· Work directly with clients to translate business problems into agent architectures, Knowledge Fabric configurations, and deployment plans.
· Deliver working, tested, governed agents, not prototypes, within realistic project timelines.
· Explain what you built to technical and non-technical stakeholders: what the agent does, what authority it has, how it is governed, how its outputs can be trusted, and how to interpret the Trust Score on every output.
· Participate in client-facing AI readiness assessments, identifying Knowledge Fabric gaps, authority level misconfigurations, and missing governance infrastructure.
Build and Improve
· Contribute to PolyPhaze's engineering practices: agent design patterns, governance templates, domain ontology extensions, and reusable MCP connection configurations.
· Stay current on MCP & A2A adoption, Knowledge Fabric capabilities, frontier & open-source model quality, and the enterprise AI stack, and bring that currency into client engagements, not just internal conversations.
What You Need
Technical Foundation
· Proficiency in Python and at least one statically typed language (TypeScript, Go, or Java). You write clean, tested, and maintainable code.
· Hands-on experience building and deploying AI agents or LLM-based systems in production, not just in notebooks or demos, in production environments where failures have consequences.
· Working knowledge of MCP: what it is, how MCP servers are implemented, what an Enterprise MCP Gateway does, and where MCP ends and the semantic layer begins.
· Experience with at least one orchestration framework (LangGraph, AutoGen, CrewAI, or equivalent). Understanding of what A2A adds and why the interim approaches accumulate technical debt.
· Familiarity with multi-model deployment: when to use frontier models, when to route to smaller or open-source models, and how to implement routing logic that makes cost visible and governable.
Knowledge Fabric and Data Architecture
· Understanding of semantic data architecture: entity resolution, referential integrity, semantic consistency across domain boundaries, and why these properties matter for agentic AI specifically.
· Ability to read and configure knowledge graph structures, semantic layer definitions, and Trust Score computation logic, not just consume them.
· Experience with at least one data platform that the Knowledge Fabric reads from: Snowflake, Databricks, BigQuery, or equivalent. Understanding of streaming, polled, and federated access patterns.
· Appreciation for why write-side governance matters and what governed writes require even if you have not yet implemented a full verification layer.
Engineering Judgment
· Ability to distinguish the layer where a failure originated, enterprise application, data platform, Knowledge Fabric, MCP layer, orchestration, or model, and direct remediation effort at the right place.
· Comfort with the governance envelope as an engineering requirement, not a compliance box: authority levels enforced architecturally, audit hooks live before the agent moves to its next step, behavioral constraints tested before production promotion.
· Honest about what you do not know and fast at closing the gap. This work spans a stack that changes every six months and requires people who update their mental models when the evidence requires it.
What Would Set You Apart
· Direct experience implementing a Knowledge Fabric or comparable semantic and trust layer above an existing enterprise data estate.
· Experience with ABAC (Attribute-Based Access Control) for agent populations, not just RBAC, and with designing authority level frameworks that hold up as agent populations scale.
· Production experience with A2A: cross-vendor agent coordination, Agent Card design, maximum authority ceiling enforcement for external agents.
· Domain depth in one or more verticals where the Knowledge Fabric provides pre-built ontologies: financial services, insurance, healthcare, manufacturing, defense.
· Experience designing AI observability infrastructure: decision tracing, ROI observability, and operational AI monitoring that catches drift before it produces consequences.
· Background in enterprise data architecture, data mesh, data products, or data warehouse design, with direct experience of the gap between data mesh governance and what agentic AI actually requires.
Compensation and Working Model
Compensation is competitive and commensurate with demonstrated production experience, not with years of service or credentials. We are indifferent to where you went to school and interested in what you have shipped.. Client engagements involve occasional travel. Flexible on full-time employment or senior contract arrangements for the right person.

Work arrangement
Yes