Chief AI Sales Engineer

Chief AI Sales Engineer

Responsibility Summary

• Set the technical vision for enterprisescale Agentic AI solutions.
• Own endtoend architecture: LLM selection, context engineering, microservices, integrations, and UI.
• Build prototypes, storyboards, demos, accelerators, and reusable agentic blueprints.
• Engineer & validate AI agents for reliability, reasoning, safety, and performance.
• Lead PoCs, pilots, and phase0 MVPs across industries.
• Create thought leadership, reusable assets, and mentor engineering teams.
• Collaborate with hyper-scalers and product partners to craft nextgeneration AI-led experiences.

Key Responsibilities (Detailed)

1. Architect & Build Cognitive Systems

• Design and implement RAG architectures (LangChain, LlamaIndex) to make enterprise-scale data conversational and intelligent.
• Build multiagent systems using LangGraph, Google ADK, Antigravity, CrewAI, AutoGen, or similar frameworks.
• Equip agents with memory, tools, planning, and context orchestration for autonomous operation.

1. Engineering & API Development

• Build high-performance RESTful APIs (FastAPI / Flask) to expose AI capabilities as secure, scalable microservices.
• Integrate LLM-backed agents with enterprise systems, retail platforms, APIs, and thirdparty tools.
• Leverage MCP and A2A architectures for system interoperability and crossagent collaboration.

1. Prompt Engineering & Guardrails

• Craft system instructions, multi-stage reasoning loops, and guardrails using NeMo Guardrails / LlamaGuard.
• Ensure safety, compliance, governance, and ethical AI in all deployments.

1. ProductionGrade Delivery

• Containerize, orchestrate, and deploy systems using Docker, with Gitbased versioning and collaboration.
• Harden prototypes and MVPs into production-ready AI services for large enterprises.

1. Innovation & Acceleration

• Work within the Retail Agent Foundry, leveraging reusable agent libraries, tools, patterns, and integrations.
• Build rapid prototypes, greenfield pilots, and highimpact, customer-facing demos.
• Continuously experiment with frontier LLMs (OpenAI o1, Gemini, Claude, etc.) and new agentic capabilities.

Required Technical Competencies (MustHaves)

• LLM-Native Foundation: Strong understanding of transformers, embeddings, tokenization, vector stores, HuggingFace ecosystem.
• Agentic Framework Expertise: Hands-on experience building real, shipped agents using LangGraph, CrewAI, Google ADK, CoPilot Studio, etc.
• Python Engineering: Proficient in building modular, scalable, production-grade Python libraries.
• Backend/API Engineering: Experience building and scaling FastAPI microservices, MCP/A2A integrations, and secure backend systems.
• DevSecOps Discipline: Hands-on with Docker, Git, CI/CD, and cloud-native workflows.
• Agentic AI Craftsmanship: Passionate builders who understand tools, memory, reasoning, and orchestration deeply.
• Engineers/architects who have shipped agentic solutions end-to-endnot just POCs.
• Strong in LLM orchestration, context engineering, microservices, API design, and integration patterns.
• Comfortable being deeply technical and client-facing.