: Chief AI Sales Engineer

Location: Chennai / Bangalore / Hyderabad

Role Type: Full‑Stack AI & Agentic Systems Engineer / Architect

Career Path: Full‑Stack AI Architect

Responsibility Summary

· Set the technical vision for enterprise‑scale Agentic AI solutions.

· Own end‑to‑end 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 phase‑0 MVPs across industries.

· Create thought leadership, reusable assets, and mentor engineering teams.

· Collaborate with hyper-scalers and product partners to craft next‑generation AI-led experiences.

Key Responsibilities (Detailed)

- Architect & Build Cognitive Systems

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

2. 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 third‑party tools.
- Leverage MCP and A2A architectures for system interoperability and cross‑agent collaboration.

3. 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.

4. Production‑Grade Delivery

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

5. Innovation & Acceleration

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

Required Technical Competencies (Must‑Haves)

· 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-end—not just POCs.

· Solid in LLM orchestration, context engineering, microservices, API design, and integration patterns.

· Comfortable being deeply technical and client-facing.

📌 Chief AI Sales Engineer (Hyderabad)
🏢 Arminus
📍 Hyderabad