SolutionAI Intelligence Lead at Contextual.io

, , United States -

Full Time

Start Date

Immediate

Expiry Date

19 Jun, 26

Salary

0.0

Posted On

21 Mar, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Information Architecture, Semantic Systems, Knowledge Representation, Embeddings, RAG, Retrieval Systems, Semantic Search, Knowledge Graphs, Ontologies, AI Infrastructure, System Architecture, Data Governance, Pattern Extraction, Solution Architecture, Technical Consulting, Workflow Automation

Industry

IT Services and IT Consulting

Description

About the Role SolutionAI is Contextual's intelligence product: the layer that helps us sell, design, build, and operate better solutions over time. It depends on a compounding knowledge backbone that gets richer with every engagement, every implementation, and every artifact our teams produce. As SolutionAI Intelligence Lead, you will own that backbone. Your job is to turn platform knowledge, delivery patterns, domain expertise, and client context into a structured intelligence product that AI can use directly. This is not a traditional documentation role. The goal is not to publish a better docs site. The goal is to build an intelligence layer that helps AI and humans find, reason over, and apply the right Contextual knowledge at the right moment. You will work at the center of engineering, solution architecture, and product. That means building the systems and structures that acquire and govern knowledge at scale, but also understanding the platform and the solution patterns deeply enough to recognize what is worth codifying in the first place. We are looking for someone who can extract what strong practitioners know implicitly, preserve the nuance that makes it valuable, and turn it into reusable intelligence. What You'll Own 1. The Context Graph Define and evolve the structure of Contextual's intelligence layer across Platform Intelligence, Domain Intelligence, and Client-Specific Context. Create schemas, relationships, metadata, and quality standards that make knowledge AI-addressable, traversable, and useful for inference rather than just searchable. Ensure the intelligence layer captures not only what exists, but why certain approaches work, where patterns apply, and what tradeoffs matter. Maintain a tiered access model across broadly reusable knowledge, proprietary Contextual IP, client-scoped context, and assets created for client value. 2. Intelligence Acquisition and Operations Design and run the ingestion and transformation systems that convert source material into usable intelligence: transcripts, solution artifacts, platform configurations, SOWs, code-adjacent technical content, implementation notes, and delivery outputs. Make intelligence capture as derivative as possible from existing work, so the system improves as a byproduct of delivery rather than relying on constant manual curation. Operate the retrieval, graph, indexing, enrichment, and quality workflows that make this intelligence reliable in production. Establish governance for versioning, lineage, freshness, quality signals, and lifecycle management. 3. Pattern Extraction from Delivery Work Work closely with cross-functional teams to identify the patterns that make solutions successful. Formalize recurring architectures, implementation approaches, operating procedures, and domain-specific heuristics into reusable intelligence assets. Recognize the difference between a one-off tactic, an emerging pattern, and something stable enough to operationalize broadly. Help Contextual preserve institutional knowledge that currently lives in meetings, heads, and scattered artifacts. 4. Enablement for AI and Solution Engineering Ensure the intelligence layer directly improves how Solution Engineers and AI systems understand existing solutions, onboard into new work, and answer technical questions. Partner with product and engineering to expose this intelligence through first-party AI surfaces, internal tools, and external development environments. Help move Contextual from ad hoc knowledge access to a more governed, repeatable, AI-native model of delivery enablement. Who You Are Deeply technical, even if you are not primarily a software engineer. You can read code, understand APIs, reason about system architecture, and follow how platform primitives compose into complete solutions. Strong in information architecture, semantic systems, knowledge representation, or a closely related discipline. You know how to turn messy real-world inputs into structured intelligence. Hands-on with embeddings, RAG, retrieval systems, semantic search, knowledge graphs, ontologies, or adjacent AI infrastructure. Passionate about AI tooling and how it changes the way great work gets done. You actively use the latest AI tools, integrate them into your own workflows, and apply AI day to day to amplify the quality and speed of your work. Strong at extracting implicit expertise from practitioners and preserving the nuance that makes it valuable. Comfortable working in ambiguity and creating structure where no mature playbook exists yet. Clear communicator who can work across product, engineering, solution delivery, and leadership. Valuable Experience Experience in a platform company, solution delivery organization, or technical consulting environment where repeatable patterns drive better outcomes. Experience building or managing knowledge graphs, semantic layers, AI-ready documentation systems, or internal intelligence products. Familiarity with workflow automation, iPaaS, low-code/no-code systems, or the kinds of platforms where solution patterns compound. Experience supporting pre-sales, solution design, implementation teams, or technical enablement through reusable knowledge systems. Experience with client-scoped knowledge, governance, or packaging internal know-how into client-facing value. Why This Role Matters Contextual is investing in a future where AI materially increases delivery leverage. That only works if the underlying intelligence is deep, structured, current, and usable. The person in this seat will help build the intelligence product underneath SolutionAI: a compounding asset that improves delivery quality, speeds onboarding, increases reuse, strengthens proprietary IP, and creates better AI experiences across the company and for every client. This is a foundational role for how Contextual turns experience into durable advantage. What We Offer Competitive salary and benefits package. Unlimited vacation where we encourage you to take it Flexible working hours and remote work options. Opportunities for professional growth and development. Collaborative and inclusive work environment. The chance to work on innovative projects with a talented team.

Responsibilities

The lead will define and evolve the structure of the intelligence layer (Context Graph) across Platform, Domain, and Client Context, creating schemas and quality standards to make knowledge AI-addressable. They will also design and operate ingestion systems to convert source materials into usable intelligence, ensuring it captures implicit expertise from practitioners.