Senior Manager, Analytics - Applied AI

本职位负责领导并管理客户分析深入研究，执行高层领导提出的策略，并处理与客户的日常沟通。
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* Job Description:

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* JOB DESCRIPTION:

** Analytics Innovation Team
** Merkle is a leading data-driven, technology-enabled, global performance marketing agency that specializes in the delivery of unique, personalized customer experiences across platforms and devices. Our Analytics Innovation team is at the forefront of implementing cutting-edge artificial intelligence solutions, from sophisticated agentic AI systems to enterprise-scale LLM deployments, with particular expertise in
** conversational analytics and text-to-SQL systems
** that transform how our clients engage with their data and customers.

The AI Engineering Lead is a critical senior technical leadership role within our growing practice. This position sits at the intersection of
** technical architecture**,
** implementation excellence**, and
** innovation leadership
** for complex AI systems. You will be responsible for designing and building production-grade AI solutions that leverage the latest technology, agentic architectures, and cloud-native infrastructure, with a strong focus on
** natural language interfaces
** and
** data democratization
** through conversational insights.

We are looking for a deeply technical leader who combines hands-on engineering expertise with strategic thinking about AI system design, including semantic layer architecture and SQL generation quality. You'll work closely with the client and our technical team to translate business requirements into scalable, reliable AI systems while establishing best practices that ensure successful deployments across our client base.
** Responsibilities:**
* ** Architect and build enterprise-scale AI systems
** including agentic workflows, RAG architectures, conversational analytics platforms, and text-to-SQL solutions that are built to scale
* ** Design and implement semantic layers
** that enable accurate natural language to SQL translation across complex enterprise data warehouses in Databricks, Snowflake, or AWS platforms
* ** Lead MLOps/Dev Ops practices
** for AI systems including CI/CD pipelines, infrastructure as code, automated testing frameworks, and production monitoring solutions
* ** Develop robust evaluation frameworks
** including golden datasets for text-to-SQL accuracy, agent quality metrics, and comprehensive system performance benchmarks
* ** Design data architectures
** for AI systems including knowledge base design, vector databases, retrieval optimization, semantic modeling, and real-time data pipelines
* ** Build conversational insights systems
** following proven methodologies: requirements gathering, semantic layer implementation, evaluation framework creation, and successful client handoff
* ** Own the technical roadmap
** for AI capabilities including evaluation of emerging technologies, proof of concepts for new approaches, and strategic partnerships with cloud providers
* ** Lead technical client engagements
** as the engineering SME for complex implementations, providing architectural guidance for both traditional AI and conversational analytics deployments
* ** Establish engineering standards
** for prompt engineering, SQL generation quality, model selection, and guardrails implementation that ensure consistent, high-quality AI experiences
* ** Mentor and develop
** a team of AI engineers while fostering a culture of technical excellence and continuous learning
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* Experience:

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* ** 5+ years
** of software engineering experience with at least
** 2 years focused on AI/ML systems
** in production environments, preferably including text-to-SQL or conversational analytics implementations
* ** Deep hands-on experience
** building LLM-based applications including prompt engineering, RAG implementations, multi-agent systems, and natural language interfaces for data
* ** Proven expertise
** in cloud platforms **(AWS/Azure/GCP)
** with specific experience in
** Databricks** (Unity Catalog, Genie) or
** Snowflake** (Cortex, Native Apps) highly preferred
* ** Strong background
** in MLOps practices and data engineering including semantic layer design, SQL optimization, and evaluation pipeline implementation
* ** Experience with modern AI…