We are seeking a highly skilled Senior Generative AI Engineer Lead to drive the design, development, and deployment of enterprise-grade Generative AI solutions. The ideal candidate will have deep expertise in Large Language Models (LLMs), prompt engineering, AI orchestration frameworks, cloud-native AI architectures, and model evaluation methodologies. This role will lead the end-to-end lifecycle of GenAI initiatives, from translating business requirements into AI-powered prototypes to delivering scalable, production-ready solutions across AWS, GCP, and Snowflake ecosystems. The candidate will also establish engineering best practices around prompt governance, model guardrails, benchmarking, and performance optimization. Generative AI Solution Design • Architect and implement enterprise-scale GenAI solutions using LLMs, foundation models, and agentic AI frameworks. • Design reusable AI patterns, accelerators, and reference architectures to enable rapid solution development. • Translate business problems into scalable AI workflows and production-ready proof-of-concepts. • Drive AI platform modernization through adoption of emerging GenAI technologies and best practices. Prompt Engineering & LLM Governance • Develop and maintain sophisticated prompt engineering frameworks for controlled and reliable LLM outputs. • Implement prompt versioning, prompt lifecycle management, and testing strategies. • Design AI guardrails to mitigate hallucinations, bias, security risks, and compliance concerns. • Establish best practices for prompt optimization, response consistency, and output quality management. AI Workflow Orchestration & Automation • Design and build scalable orchestration pipelines using frameworks such as LangGraph, LangChain, CrewAI, Semantic Kernel, or equivalent. • Develop reusable AI components, tools, agents, and workflow templates for enterprise adoption. • Implement multi-agent systems and autonomous workflows to support complex business use cases. Prototyping & Business Enablement • Partner with business stakeholders to identify high-value AI opportunities. • Rapidly develop AI prototypes and MVP solutions using synthetic and enterprise datasets. • Convert prototypes into production-ready applications adhering to scalability, security, and reliability standards. Cloud & Data Engineering • Build scalable GenAI architectures across AWS, GCP, and Snowflake platforms. • Leverage cloud-native AI services including Amazon Bedrock, SageMaker, Vertex AI, Snowflake Cortex AI, and related ecosystems. • Design robust RAG (Retrieval-Augmented Generation) architectures incorporating vector databases, embeddings, and semantic search. • Optimize model deployment, inference performance, and infrastructure cost efficiency. Evaluation & Performance Optimization • Establish AI evaluation frameworks to measure accuracy, relevance, latency, safety, and business impact. • Develop benchmarking methodologies for comparing prompts, models, and workflows. • Define KPIs, observability frameworks, and monitoring strategies for GenAI applications. • Continuously improve model performance through prompt tuning, retrieval optimization, and workflow enhancements. • Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related field. • 8+ years of experience in software engineering, machine learning, data engineering, or AI solution development. • 4+ years of hands-on experience with Generative AI, LLMs, and foundation models. • Strong experience designing enterprise GenAI solutions utilizing advanced LLM architectures and prompt frameworks. • Expertise in building scalable AI workflows, orchestration pipelines, and reusable AI components. • Proven ability to translate business requirements into production-ready AI solutions and prototypes using synthetic data. • Deep understanding of prompt versioning, prompt governance, guardrails, and controlled LLM outputs. • Strong knowledge of AI evaluation frameworks, benchmarking methodologies, and optimization techniques. • Experience with vector databases, embeddings, semantic search, and RAG architectures. • Strong proficiency in Python and modern AI/ML development frameworks.