• 5+ years of professional experience in AI/ML development, software engineering, or testing. • Hands-on experience with Copilot, Claude, OpenAI, or other LLM-based platforms. • Dedicated testing and QA experience within AI/ML systems. • Proven experience delivering production-grade AI applications. • Experience working with multi-model AI ecosystems across OpenAI, Anthropic, Microsoft, Google, Meta, and similar platforms. • Hands-on experience in designing, developing, and deploying AI-powered tools and applications in enterprise environments. • Demonstrated experience implementing and managing productionized AI solutions, including monitoring, governance, scalability, and performance optimization. • Experience implementing RAG (Retrieval Augmented Generation) solutions and integrating with vector databases. • Exposure to MCP (Model Context Protocol) servers and AI agent frameworks. AI & Machine Learning Technologies • LangChain • LlamaIndex • OpenAI APIs • Anthropic APIs • Azure OpenAI • Microsoft Copilot • Prompt Engineering • RAG (Retrieval Augmented Generation) • MCP (Model Context Protocol) Servers • AI Guardrails implementation and validation • Context Compression and Token Optimization techniques Key Responsibilities AI Development & Implementation • Build and maintain enterprise-grade AI tools leveraging LLMs, Copilots, and AI agent frameworks. • Implement AI Guardrails to ensure application safety, compliance, responsible AI usage, and security. • Design and optimize Context Compression strategies to reduce token consumption, improve performance, and control operational costs. • Develop and integrate RAG pipelines and MCP Server-based solutions to enable contextual and scalable AI experiences. • Deploy, monitor, and maintain productionized AI solutions with appropriate observability, governance, and feedback mechanisms. The candidate should possess a strong understanding of: • Large Language Models (LLMs) • Generative AI solution architecture • Retrieval Augmented Generation (RAG) • Vector Databases • NLP/NLU concepts • AI Ethics and Responsible AI • Bias Detection and Mitigation • AI Security and Data Privacy • Regulatory Compliance Requirements • AI Guardrails, content filtering, prompt injection prevention, and responsible AI controls • Context Compression, token management, and cost optimization strategies • MCP (Model Context Protocol) architecture and AI agent integrations • Best practices for production deployment, monitoring, and governance of AI solutions Required Technical Competencies AI & LLM Expertise • Microsoft Copilot Suite • GitHub Copilot • Claude (Opus, Sonnet, Haiku) • OpenAI GPT Models • Prompt Engineering • Few-shot Learning Techniques • Model Selection & Evaluation • Hands-on experience building AI tools and enterprise AI applications • Design and implementation of AI Guardrails • Context Compression and Token Optimization techniques • RAG architecture design and implementation • MCP Server integration and configuration • Production deployment and lifecycle management of AI solutions • Experience implementing AI Guardrails and Responsible AI frameworks in enterprise environments. • Hands-on experience optimizing LLM applications for token efficiency, latency, and cost management. • Experience working with MCP servers, AI agents, and multi-agent architectures. • Experience deploying and supporting productionized AI solutions at scale. • Experience building custom AI copilots, assistants, and enterprise AI tools.