Gen AI Engineer Lead

W2 only Contract Role AI Engineer Lead 6 months contract Remote (Can work anywhere is US) Client is seeking a senior AI Engineer Lead (Contractor) to join our Gen AI team supporting Brand Growth Systems (BGS). In this role, you will partner closely with Business stakeholders, the Gen AI Center of Excellence (COE), Data Science, Machine Learning, and Platform teams to design, develop, and deploy an AI orchestration layer that powers insight generation across BGS. This is a hands-on technical leadership role at the center of our AI maturity journey evolving our insight capabilities from proactive, to agentic, to autonomous. You will help define the architecture, patterns, and engineering standards that enable intelligent, scalable, and production-grade AI solutions on Azure. Key Responsibilities Lead the design, development, and deployment of the AI orchestration layer for Brand Growth Systems, enabling automated and intelligent insight generation. Build the orchestration layer across three core capability sources: Snowflake Semantic Views leveraging Cortex Analyst for natural-language access to structured/governed data. Deployed ML models integrating predictive and prescriptive models developed by the Data Science / ML teams. Unstructured documents enabling retrieval and reasoning over playbooks and other unstructured content. Partner with Business teams, the Gen AI COE, Data Science, ML, and Snowflake Platform teams to translate business needs into robust, scalable AI engineering solutions. Architect and implement agentic AI workflows advancing capabilities from proactive insights toward agentic and ultimately autonomous systems. Integrate LLM-based components, ML models, and data pipelines into cohesive, production-ready services on Azure. Establish engineering best practices for AI solution development, including evaluation, monitoring, observability, security, guardrails, and responsible AI standards. Implement guardrails around AI-generated responses including grounding, output validation, and content safety controls to govern response quality and protect user trust. Provide senior-level technical guidance and mentorship to engineers and cross-functional partners. Drive rapid prototyping and iteration while maintaining a clear path to production and enterprise-grade quality. Communicate architecture decisions, trade-offs, and progress clearly to both technical and business stakeholders. Required Qualifications 8+ years of software/AI/ML engineering experience, with 2+ years in a technical lead role. Proven experience designing and deploying AI/ML and Generative AI solutions in production, ideally including orchestration frameworks and agentic architectures (e.g., LangChain, LangGraph, Semantic Kernel, AutoGen, or similar). Strong hands-on experience with the Azure ecosystem (e.g., Azure OpenAI, Azure ML, Azure Functions, AKS, Azure Data services). Experience with Snowflake, including Semantic Views and Cortex Analyst (or comparable text-to-SQL / semantic layer technologies).Solid foundation in machine learning concepts, MLOps/LLMOps practices, and model lifecycle management, including integrating deployed ML models into orchestrated AI workflows. Experience building RAG / document intelligence pipelines over unstructured content (e.g., playbooks, guidelines, knowledge bases).Strong programming skills in Python; experience building APIs and microservices. Experience working cross-functionally with data science, platform, and business teams in a large enterprise environment. Experience implementing guardrails for AI systems (e.g., output validation, grounding, content filtering, and hallucination mitigation) to govern responses and protect user trust. Excellent communication skills and the ability to operate with autonomy and ownership. Preferred Qualifications Experience with RAG pipelines, vector databases, and prompt/agent evaluation frameworks. Familiarity with CPG, retail, or consumer insights/brand analytics domains. Experience establishing responsible AI, governance, and security practices for enterprise AI systems. Background in scaling AI capabilities within a Center of Excellence or platform team model. Experience working with globally matrixed teams.