Principal, GenAI Context Engineering & Architecture (US AI GTM Technical Lead)

Principal, GenAI Context Engineering & Architecture (US AI GTM Technical Lead)

About Us

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.

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.

Role Overview

We are seeking a Principal-level GenAI leader to serve as the senior technical lead in our US AI Go-To-Market team for two offerings - Establish AI-Ready Data and Context Foundation, and Scale and Operationalize AI for the Enterprise - across CPG, Retail, Pharma and other industries. This individual will bring deep expertise in modern Generative AI architectures and, equally importantly, the ability to translate that expertise into scalable applications that solve complex business problems.

This is not a purely theoretical AI or research role. We are looking for someone who has designed and implemented production AI solutions and understands what it takes to move from experimentation and proofs of concept to enterprise-scale applications.

The Principal will play a critical role in client conversations, solutioning, and architecture design, serving as a credible technical authority with both business and technology executives. They will help define how we architect GenAI solutions, establish technical credibility with clients, and guide teams through the decisions required to successfully build and scale AI. The role sits in live deals: it partners with the GTM orchestrator, who owns the commercial motion, from first meeting through proposal defense, and with MathCo’s India-based architects and solutioning leads so that what we sell is executable.

The ideal candidate has worked deeply with LLMs, knowledge graphs, context engineering, agentic systems, AI orchestration/harnesses, agent skills and modern AI platforms, including model providers such as OpenAI, Anthropic and Google, and data platforms such as Databricks, Snowflake and Google Cloud.

Role & Reporting

• Reports to the Head of AI GTM (US). Works alongside the GTM orchestrators (CPG / Retail and Pharma): they own the commercial motion; this role owns the technical motion.
• Design partnership with MathCo’s CTO office, AI practice and Labs for architecture review and depth.
• Takes over the technical lead on data & context foundation and AI-scale deals from the CTO / Co-founders, who move from prerequisite to final review.
• Carries 6–8 active deals at solution stage at any time, with at least 70% of time on client and deal work.
• Based in Chicago or New York, with regular client travel.

What You’ll Do

Serve as a GenAI Technical Authority

• Serve as a senior subject matter expert for Generative AI, providing technical credibility in strategic client and solutioning conversations.
• Engage confidently with CTOs, CIOs, Chief Data Officers, AI leaders, architects, and business executives.
• Translate highly technical AI concepts into clear business implications, tradeoffs, and recommendations.
• Represent the organization in complex technical discussions where clients expect deep, practical expertise rather than high-level AI knowledge.
• Partner with senior leadership to deepen the organization's GenAI capabilities and technical credibility.
• Answer the follow-up questions when clients probe context layers, knowledge graphs, agents and AI operations — live, in the room, rather than deferring to a later call.

Architect Enterprise GenAI Solutions

• Define and articulate end-to-end architectures for sophisticated GenAI applications.
• Determine how components such as LLMs, knowledge graphs, retrieval and context layers, agents, tools, agent skills, orchestration frameworks, and enterprise data should work together.
• Develop architecture patterns that enable AI applications to move beyond prototypes into scalable, production-ready solutions.
• Evaluate architectural options and make informed recommendations based on business requirements, performance, scalability, security, maintainability, and cost.
• Provide technical direction to engineering and delivery teams without necessarily serving as the primary day-to-day developer, while being able to build a working prototype or demo when a deal needs one.
• Author the technical sections of proposals — architecture, phasing, assumptions and effort estimates — and defend them with clients.
• Build reusable deal assets: reference architectures, scoping frameworks and stack-specific demos (e.g., Databricks, Google Cloud).

Connect AI to Business Processes

• Work with clients to understand business processes and identify where GenAI can materially transform how work gets done.
• Move conversations beyond individual use cases or models to understand the business process, application, context, data, and architecture required for successful implementation.
• Partner with business and consulting teams to shape AI opportunities into technically credible solutions.
• Help clients understand the journey from experimentation to implementation and ultimately enterprise scale.
• Build the business case: quantify the value and ROI of context layers and AI programs in terms business and finance leaders accept.

Advance Context and Agentic AI Capabilities

• Bring deep knowledge of how context is assembled and managed for modern AI systems, including structured and unstructured enterprise knowledge.
• Design solutions involving knowledge graphs, retrieval systems, contextual data, memory, tools, and other mechanisms that improve the relevance and reliability of LLM-based applications.
• Design the data foundation under AI applications: lakehouse and data-platform architecture, metadata and catalogs, semantic layers, data quality and access controls.
• Architect agentic systems, including the tools, skills, orchestration, guardrails, and supporting infrastructure required for agents to perform meaningful enterprise workflows.
• Stay close to advances in foundation models and the broader GenAI ecosystem, translating emerging capabilities into practical enterprise solutions.

Guide Implementation and Scale

• Bring an implementation-oriented perspective to architecture decisions based on firsthand experience building and deploying AI solutions.
• Provide technical guidance throughout solution design, prototyping, implementation, and scaling.
• Partner closely with AI scientists, engineers, architects, product teams, and client stakeholders.
• Establish reusable architecture patterns, technical standards, and best practices that raise the capabilities of the broader organization.
• Mentor teams and help develop deeper GenAI expertise across the organization.

Operationalize AI at Enterprise Scale

• Shape AI operations solutions: LLMOps / AIOps, evaluation and monitoring, guardrails, and model and agent lifecycle management.
• Advise on AI cost: token usage tracking and optimization, model selection and routing, and infrastructure cost trade-offs.
• Advise on AI governance and risk: frameworks such as NIST AI RMF and the EU AI Act, data privacy (including HIPAA for Pharma), security, access control and audit.
• Develop points of view and client narratives on emerging AI-at-scale topics with MathCo’s COE and Labs.

What We’re Looking For

• 10 years of professional experience in Artificial Intelligence, Generative AI, Machine Learning, AI Engineering, or a closely related discipline, including 2 years designing and shipping production GenAI / LLM applications.
• Demonstrated experience architecting and implementing GenAI applications, not just researching, advising on, or experimenting with the technology.
• Deep understanding of large language models and the modern GenAI application stack.
• Hands-on implementation experience with sophisticated AI applications using commercial and/or open-source foundation models.
• Strong knowledge of areas such as:
• LLM-based application architecture
• Context engineering and context management
• Knowledge graphs
• Retrieval and grounding approaches
• Agentic AI and multi-step AI workflows
• Agent tools and skills
• AI orchestration and harnesses
• Enterprise data integration
• Evaluation, observability, governance, and responsible AI
• Data platforms and lakehouse architecture (e.g., Databricks, Snowflake, BigQuery)
• Semantic layers, metadata and catalogs
• LLMOps, AI cost (token) optimization and AI platform operations
• AI governance frameworks, privacy and security
• Familiarity with leading AI platforms and model ecosystems, including OpenAI, Anthropic and Google, and hyperscaler / data-platform AI services; able to stay model-agnostic in recommendations.
• Ability to independently develop and communicate sophisticated AI architecture and explain why specific technical choices were made.
• Experience taking AI applications from concept or prototype through implementation and production scale.
• Strong client-facing communication skills and the ability to establish credibility quickly with senior technical stakeholders.
• Ability to move fluidly between business process discussions, technical architecture, and implementation considerations.
• Experience guiding multidisciplinary teams through technically complex AI engagements.
• Pre-sales, solutions engineering or consulting experience: has authored and defended the technical sections of proposals and SOWs.
• Experience working with offshore / India-based architecture and delivery teams.
• Exposure to data and AI use cases in at least one of CPG, Retail or Pharma / Life Sciences.

What Will Differentiate You

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

1. Technical depth

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

2. Architecture and implementation experience

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

3. Executive and client credibility

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.

We are not looking for someone whose experience is limited to AI strategy or theory. This individual should have firsthand experience building or implementing modern AI solutions and be able to draw on those experiences when advising clients and teams.

What Success Looks Like

In this role, success means becoming a trusted technical authority for our teams and clients. You will raise the technical bar of our GenAI solutioning, enable more sophisticated client conversations, establish scalable architecture patterns, and help translate rapidly evolving AI capabilities into real enterprise applications.

Over time, you will help build an organization where deep GenAI technical credibility is embedded throughout the business rather than concentrated among a small number of senior technology leaders.

Specifically, success is measured by:

• Within 90 days: leads the technical motion on all active US data & context foundation and AI-scale deals; CTO / Co-founder involvement moves to final review on standard deals.
• Produces credible proposal architectures independently, scoped for what India delivery can execute.
• Within 180 days: a reusable context-layer reference architecture, ROI model and stack-specific demos in active use by the GTM team, and a first AI-at-scale (operations, cost or governance) point of view taken to clients.

What This Role Is Not

• Not a delivery IC - hands off to delivery at kickoff and stays involved through the transition.
• Not a research role - the focus is applied enterprise AI.
• Not the GenAI practice or people lead — mentoring is part of the role; running the AI practice sits with MathCo’s CTO office and practice leadership.
• Not the commercial owner of deals — the GTM orchestrator owns cadence, commercials and follow-up.

Being a Mathemagician

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

Why Join Us?

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

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