Principal AI Solutions Architect

Job Description

Principal AI Solutions Architect

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About Us

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MathCo® (TheMathCompany) is a global Enterprise AI and Analytics company trusted by Fortune 500 and Global 2000 enterprises for data-driven decision-making. Founded in 2016, MathCo builds custom AI and advanced analytics solutions focused on enterprise problem-solving through its innovative hybrid model. NucliOS, MathCo’s proprietary platform with pre-built workflows and reusable plug-and-play modules, enables the vision of connected intelligence at a lower TCO.

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We foster an open, transparent, and collaborative culture with no barriers, making MathCo a great place to work. We offer exciting growth opportunities and value capabilities and attitude over experience-enabling our Mathemagicians to truly Leave a Mark.

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Role Overview

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We are seeking a Principal AI Solutions Architect with deep, hands-on experience building production GenAI and agentic systems. You will be the senior technical voice on our US AI engagements focused on AI-ready data and context foundations and on operating AI on an enterprise scale, across CPG, Retail, Life Sciences and other industries, turning that expertise into scalable applications that solve complex business problems.

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This is not a research role. We are looking for someone who has designed and implemented production AI solutions and knows what it takes to move from POCs to enterprise-scale applications.

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You will work on client opportunities from the first conversation through proposal, alongside our client and sales leaders, and with MathCo’s global architecture and delivery teams, including our teams in India, so that every solution is technically sound and deliverable.

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The ideal candidate has worked deeply with LLMs, knowledge graphs, context engineering, agentic systems, AI orchestration, and modern AI and data platforms.

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What You’ll Do

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Serve as a GenAI technical authority

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• Provide senior technical credibility in strategic client and solutioning conversations with CTOs, CIOs, Chief Data Officers, AI leaders and business executives.
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• Translate complex AI concepts into clear business implications, trade-offs and recommendations.
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• Lead in-depth technical discussions on context layer, knowledge graphs, agents and AI operations in live client settings.
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Architect enterprise AI solutions

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• Define end-to-end architectures that bring together LLMs, knowledge graphs, retrieval and context layers, agents, tools, orchestration frameworks and enterprise data.
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• Develop architecture patterns that take AI applications from prototype to production.
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• Evaluate options against business requirements, performance, scalability, security, maintainability and cost.
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• Provide technical direction to engineering and delivery teams, and build a working prototype or demo when needed.
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• Author and present the technical sections of proposals: architecture, phasing, assumptions and effort estimates.
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• Build reusable reference architectures, scoping frameworks and platform-specific demos.
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Connect AI to business outcomes

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• Work with clients to understand business processes and identify where AI can materially change how work gets done.
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• Partner with business and consulting teams to shape AI opportunities into technically viable solutions.
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• Build the business case: quantify the value and ROI of AI programs
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Design the data and context foundation

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• Design how context is assembled and managed for AI systems across structured and unstructured enterprise knowledge.
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• Design solutions using knowledge graphs, retrieval, memory, tools and other mechanisms that improve the relevance and reliability of LLM applications.
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• Design the data foundation under AI applications: lakehouse and data-platform architecture, metadata and catalogs, semantic layers, data quality and access controls.
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• Architect agentic systems, including tools, orchestration, guardrails and supporting infrastructure.
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Operationalize AI at enterprise scale

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• Shape AI operations solutions: LLMOps, evaluation and monitoring, guardrails, and model and agent lifecycle management.
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• Advise on AI cost: token usage and optimization, model selection and routing, and infrastructure trade-offs.
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• Advise on AI governance and risk, including frameworks such as NIST AI RMF and the EU AI Act, data privacy (including HIPAA), security and audit.
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• Develop points of view on emerging AI-at-scale topics with MathCo’s innovation teams.
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What We’re Looking For

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• 10+ years in AI, machine learning, data or enterprise architecture, including 2+ years designing and shipping production GenAI / LLM applications.
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• Hands-on experience building sophisticated AI applications with commercial and/or open-source foundation models.
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• Strong knowledge of LLM application architecture, context engineering, knowledge graphs, retrieval and grounding, agentic AI and multi-step workflows, AI orchestration, and enterprise data integration.
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• Working knowledge of lakehouse and data platforms (e.g., Databricks, Snowflake, BigQuery), semantic layers, metadata and catalogs.
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• Working knowledge of evaluation, observability and LLMOps, AI cost optimization, and AI governance, privacy and security.
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• Familiarity with leading model ecosystems (OpenAI, Anthropic, Google) and hyperscaler AI services
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• Pre-sales, solutions engineering or consulting experience, including authoring and defending the technical sections of proposals and SOWs.
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• Ability to explain why specific technical choices were made, to engineers and executives alike.
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• Experience working with global delivery teams across time zones.
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• Exposure to data and AI use cases in CPG, Retail or Life Sciences.
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What Will Differentiate You

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The strongest candidates will be able to demonstrate all three of the following:

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1. Technical depth

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You understand the mechanics of modern AI systems deeply enough to engage with senior AI engineers, scientists and architects.

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2. Architecture and implementation experience

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You can whiteboard an end-to-end AI architecture and explain how it would actually be implemented, including the decisions and trade-offs required to make it successful at enterprise scale.

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3. Executive and client credibility

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You can take that same architecture into a client conversation and explain it in a way that builds confidence with both business and technical executives — including a value case a CFO would sign off on.

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What Success Looks Like

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• You are the trusted technical voice on our AI data, context and scale engagements.
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• Your proposal architectures are credible, clearly scoped and deliverable.
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• The reference architectures, ROI models and demos you build are used across the team.
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• You bring new points of view on operating AI at scale to our clients.
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Being a Mathemagician

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• Embody MathCo’s values of openness, ownership, and impact.
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• Contribute actively to internal mentorship, innovation, and team development.
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• Champion diversity, equity, and inclusion by valuing a variety of perspectives in problem-solving.
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Why Join Us?

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MathCo is recognized globally as a leader in enterprise AI and analytics:

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• Leader in Generative AI Service Providers – PeMa Quadrant (AIMResearch)
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• Named among North America’s Inspiring Workplaces
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• Accredited for Inclusive Practices – Great Place to Work Institute, India
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• Recognized for DEIB Excellence – India’s Best Workplaces™ for DEIB
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• To learn more, visit
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