AI Evangelist / Sr AI Engineer

Lead enterprise adoption of Generative AI by building production-grade AI systems and educating teams on practical, responsible use cases.

Responsibilities

• Design, develop, and implement enterprise-grade AI and Generative AI applications
• Build scalable LLM-based solutions using LLMs, multimodal models, AI agents, RAG, vector databases, knowledge graphs, APIs, and enterprise data platforms
• Architect AI systems for enterprise-scale workloads with focus on scalability, reliability, security, performance, observability, cost optimization, and governance
• Develop advanced Retrieval-Augmented Generation (RAG) architectures including hybrid search, reranking, metadata filtering, semantic retrieval, and knowledge grounding
• Build multi-agent and agentic AI systems for reasoning, tool usage, workflow execution, orchestration, and integration with enterprise systems
• Integrate structured and unstructured enterprise information into AI applications
• Evaluate architecture choices across commercial models, open-source models, small language models, domain-specific models, and hosted AI platforms
• Implement model routing, prompt orchestration, caching, guardrails, evaluation pipelines, and fallback strategies
• Collaborate with cloud, platform, security, enterprise architecture, DevOps, and application engineering teams
• Develop production-grade applications using leading commercial and open-source LLM ecosystems
• Design scalable LLM platforms supporting multiple enterprise applications and use cases
• Create reusable LLM services, APIs, components, agent frameworks, and reference architectures
• Design and implement AI agents and agentic workflows to automate complex business and engineering processes
• Build agents that can interact with enterprise applications, APIs, databases, documents, development environments, and workflow platforms
• Evaluate emerging agent architectures and orchestration frameworks pragmatically
• Develop reusable patterns for single-agent systems, multi-agent systems, human-in-the-loop workflows, autonomous workflows, tool-calling agents, coding agents, and enterprise knowledge agents
• Establish controls for AI agent identity, permissions, auditability, security, and human oversight
• Drive AI adoption across the Software Development Life Cycle
• Build advanced AI prototypes and demonstrations ahead of formal enterprise implementation
• Experiment with emerging AI models, agents, frameworks, development tools, and architectures
• Convert emerging technology into demonstrable business scenarios
• Build solutions using APIs, SDKs, terminals, development environments, cloud platforms, and enterprise applications
• Create demonstrations explaining what can reliably be achieved today versus what remains experimental
• Identify limitations and edge cases before production rollout
• Feed technical learnings back into enterprise architecture, product strategy, AI standards, and engineering practices
• Serve as a visible internal champion for responsible and effective AI adoption
• Educate engineering and business teams about practical AI opportunities
• Demonstrate emerging AI

Requirements

• Minimum experience: 8+ years
• Education: BE/BTech
• Machine Learning; Artificial Intelligence; Deep Learning; Artificial Intelligence Engineer; Artificial Intelligence Developer
• AI Solutions; AI Platform; AI Techniques
• Azure Cognitive Services

Role Focus Areas

• Core AI Engineering & Architecture
• Deep AI / ML / DL Expertise
• Generative AI & LLM Engineering
• AI Agents & Automation
• AI-Enabled Software Engineering
• Build Frontier AI Demonstrations
• AI Evangelism & Enterprise Adoption

Strong Technical Understanding Across Modern AI

• Artificial Intelligence, Machine Learning, Deep Learning, Neural Networks
• LLM / SLM / RAG, Graph RAG
• MLOps / LLMOps / GenAIOps

Department: ETI

Open positions: 1

Location: San Antonio, TX (onsite)

Salary: USD 400,000 - 5,000,000 per yearly

Posted on: 17-Sep-2026