AI Architect

Go Fractional; Northern (KY); Hybrid; USD 180,000 - 260,000; Full time

Job summary

Go Fractional is seeking an AI Architect to design and build the intelligence engine at the core of our platform, and the conversational AI agents that present results in natural language. The role demands hands-on leadership, strong grounding, and auditable outputs from verified data sources.

You will set technical direction for the engine and agent layer, partner with US-based AI leadership, and ensure end-to-end evaluation and CI-based quality.

Qualifications

• Shipped production AI systems and led technical direction.
• Experience with retrieval-augmented generation and structured output.
• Built multi-step agent workflows with verification and fallbacks.
• Proficient in Python and production services with SQL access.
• Document architecture decisions and run design reviews.

Responsibilities

• Design and build the intelligence engine for the platform.
• Lead the agent layer and ensure deterministic, auditable results.
• Collaborate with US-based AI leadership on grounding and evaluation.
• Establish data interfaces, testing, and CI-driven evaluation.
• Mentor engineers and communicate architecture trade-offs clearly.

Skills

Senior AI/ML

LLM patterns

Agent orchestration

Systems architecture

Grounding evaluation

Determinism

Model judgment

Python development

Retrieval tooling

Technical leadership

Strong communication

Flexible engagement

Tools

LangGraph

pgvector

Pinecone

Weaviate

Qdrant

Job description

The Role

We are looking for an AI Architect to help design and build the intelligence engine at the core of our platform, and the conversational AI agents that sit on top of it and serve answers deterministically.

Our output sits adjacent to externally reported financials. \"Sounds plausible\" is never good enough. The engine you help architect turns connected enterprise data into answers that are computed, reproducible, and traceable to verified source data — not improvised by a model. The agents that sit on top interpret what a user is asking, route it to the engine, and present the result in natural language without ever inventing the substance of the answer.

This is a deeply hands-on architecture role. You set technical direction for the engine and agent layer, partner closely with US-based AI leadership and the Staff AI Engineer on grounding and evaluation rigor, and raise the bar for how deterministic, auditable AI gets built across the team.

What makes this role different

• Deterministic by design — the engine computes answers from verified data. The same question against the same data returns the same grounded, explainable answer.
• A clear separation of concerns — the engine reasons and decides; the conversational agents orchestrate and narrate, but never fabricate the substance.
• Auditable end to end — every answer traces back to its source data and the logic that produced it, because our customers' auditors will ask.
• Deploys in customer clouds — including managed LLMs in-VPC, so customer data never leaves their environment.

Requirements

• Senior or staff-level applied AI/ML experience. You have shipped production AI systems, not just prototypes, and have led the technical direction on at least one of them.
• Deep fluency in modern LLM patterns. Hands-on experience with retrieval-augmented generation (RAG), tool-using agents, and structured output (e.g., JSON schemas, function calling).
• Agent orchestration. You have built multi-step agent workflows with LangGraph or a similar framework, including verification steps, fallbacks, and points where a human confirms before the system proceeds.
• Systems architecture. You can design an engine, its data interfaces, and the agent layer on top as one coherent, maintainable system, and document decisions (e.g., architecture decision records).
• Grounding, hallucination control, and evaluation. You have made \"no unsupported answers\" a hard rule in a real system, and measured faithfulness and hallucination with automated evaluations, ideally running in CI.
• Determinism on top of probabilistic models. You know how to make the same question against the same data return the same answer, and can explain the techniques you used.
• Clear judgment about what a model can legitimately claim. You understand why computed, traceable answers matter more than plausible-sounding ones in a high-stakes domain.
• Production software engineering in Python. You write robust, tested services with SQL access to warehouses or databases (e.g., Snowflake, PostgreSQL), and you are comfortable with containerized deployment.
• Structured data and retrieval tooling. Working knowledge of vector stores (pgvector, Pinecone, Weaviate, or Qdrant) and of computing reliable answers over structured data.
• Technical leadership. You mentor engineers, run design reviews, and turn strategy set by US leadership into well-built execution.
• Strong communication. You can explain architecture trade-offs to engineers, product managers, and non-technical stakeholders in clear English.
• Location and working hours. Based in Hyderabad (preferred) or remote elsewhere, and able to keep a few hours of daily overlap with US time zones.
• Flexible engagement. Open to full-time, contract, or contract-to-hire, whichever suits you best.

Nice-to-Haves

• Finance, accounting, or other audited domains. Experience where outputs face auditors or regulators (finance, healthcare, insurance, legal).
• Knowledge graphs and reasoning engines. Experience with knowledge graphs, semantic layers, or rule/reasoning engines that model facts and relationships.
• Human-in-the-loop systems. You have designed review, confirmation, or feedback loops that improve a system over time.
• Managed LLMs inside customer clouds. Hands-on with Amazon Bedrock, Google Vertex AI, or Azure OpenAI deployed in a customer's private network (VPC), so data never leaves their environment.
• Multi-cloud and on-prem deployment. Experience shipping AI to AWS, Azure, and GCP, including single-tenant and on-premises installs, and working with platform teams on model serving and inference cost.
• AI observability. Experience with tracing and evaluation tooling for LLM applications.