AI Evangelist / Sr AI Engineer

Key Responsibilities: We are seeking a highly experiencedAI Evangelist & Core AI Engineerwho combines deep technical expertise in Artificial Intelligence with the ability to influence, educate, demonstrate, and accelerate AI adoption across the enterprise. This is not primarily an advisory role. The successful candidate will be expected todesign, build, demonstrate, deploy, and scale real AI solutions, while simultaneously acting as an internal AI champion who helps engineering teams, business leaders, architects, and delivery organizations understand how emerging AI technologies can create measurable business value. The role requires deep understanding ofAI, Machine Learning, Deep Learning, Generative AI, Large Language Models, Agentic AI, AI engineering, and modern software development practices, combined with significant hands‑on experience implementing scalable AI applications in complex enterprise environments. The individual should be comfortable moving from a business problem to architecture, prototype, production implementation, enterprise standards, reusable reference patterns, and organization‑wide adoption.1. Core AI Engineering & Architecture Design, develop, and implement enterprise‑grade AI and Generative AI applications. Build scalable applications usingLLMs, multimodal models, AI agents, RAG, vector databases, knowledge graphs, APIs, and enterprise data platforms .Architect AI solutions capable of supporting enterprise‑scale workloads with appropriate considerations for: Scalability ReliabilitySecurityPerformanceObservabilityCost optimization GovernanceDevelop and implement advanced Retrieval-Augmented Generation (RAG)architectures including hybrid search, reranking, metadata filtering, semantic retrieval, and knowledge grounding. Build multi‑agent and agentic AI applications capable of reasoning, tool usage, workflow execution, orchestration, and enterprise system integration. Design AI applications integrating structured and unstructured enterprise information. Evaluate architecture choices between 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. Work closely with cloud, platform, security, enterprise architecture, DevOps, and application engineering teams.2. Deep AI / ML / DL Expertise The candidate should have strong conceptual and practical understanding across the modern AI technology landscape, including: Artificial Intelligence /Machine Learning/ Deep Learning/ Neural Networks /LLM/SLM/RAG/Graph RAG / MLOps / LLMOps / GenAIOpsBuild production‑grade applications utilizing leading commercial and open‑source LLM ecosystems. Design scalable LLM platforms supporting multiple enterprise applications and use cases. Develop reusable LLM services, APIs, components, agent frameworks, and reference architectures.4. AI Agents & Automation Design and implementAI agents and agentic workflowsthat automate complex business and engineering processes. Build agents capable of interacting with enterprise applications, APIs, databases, documents, development environments, and workflow platforms. Evaluate emerging agent architectures and orchestration frameworks pragmatically. Single-agent systems Multi-agent systems Autonomous workflows Tool‑calling agents Coding agents Establish appropriate controls for AI agent identity, permissions, auditability, security, and human oversight.5. AI-Enabled Software Engineering Drive adoption of AI throughout the Software Development Life Cycle.6. Build Frontier AI Demonstrations A critical part of the role is demonstrating what is technically possible. The individual will: Build advanced AI prototypes and demonstrations before formal enterprise implementation. Experiment with emerging AI models, agents, frameworks, development tools, and architectures. Convert emerging technology into demonstrable business scenarios. Build solutions directly using APIs, SDKs, terminals, development environments, cloud platforms, and enterprise applications. Create demonstrations that explain both: What technology can reliably achieve today What remains experimental or emerging Identify limitations and edge cases before solutions reach production. Feed technical findings back into enterprise architecture, product strategy, AI standards, and engineering practices. Act as a visible internal champion for responsible and effective AI adoption.