Machine Learning Engineers work to deploy end-to-end solutions to business problems leveraging AI and/or ML principles as needed to create those solutions. MLEs will take requests from stakeholders, define the components required for the project, gather data necessary for project EDA and training, then work with stakeholders to develop a plan around the productionized use of the solution, and work to put that solution into final production.
Responsibilities

• Consult with stakeholders to gather business requirements, translate them into agentic AI and data solutions, design high-level agent and model architechures, and demonstrate deep expertise in advanced analytics LLMs and AI/ML techniques to design, prototype, and build production-grade solutions to business problems.
• Architect, build, and deploy agentic AI systems (single-agent and multi-agent workflows) on Google Cloud, leveraging Google’s Customer Engagement Suite (CES), Vertex AI Agent Builder, and Gemini-family models to automate enterprise workflows in customer engagement, sales, marketing, and operations.
• Design, integrate, and orchestrate the tools, APIs, function calls, retrieval pipelines (RAG), memory stores, and guardrails that extend agent capabilities, and own the end-to-end deployment, observability, evaluation, and lifecycle management of these agents in production.
• Lead communication with other stakeholders to drive agentic use case development and manage expectations on model and agent limitations, latency, cost, and lead times.
• Analyze data to identify useful relations, patterns and features that are predictive of user behaviors, preferences, intents, and interests, and use these signals to ground and personalize agent behavior.
• Manage and execute entire projects from start to finish, including cross-functional project management; data collection and manipulation, analysis and modeling; communication of insights and recommendations; productionalization of final model and agent products.
• Share findings with stakeholders to improve business decisions and/or influence strategic direction.
• Monitor and stay updated with industry trends and emerging technologies in agentic AI, foundation models, and MLOps/AgentOps to identify opportunities for innovation and improvement.
• Develop and maintain end-to-end modeling and agent code, and standardize the code for reusability in the production environment.
• Profile users including customer segmentation to help the marketing team target specific audiences for upgrading services and for user retention, and operationalize these insights through agent-driven engagement.

Qualifications

• Graduate Degree in a quantitative discipline, such as Data Science, Applied Mathematics, Statistics, Economics, Operations Research, Computer Science, Mathematics, Physics, Biology, Chemistry or Engineering. Phd is a plus
• 3-5 years of work experience in classification, regression, clustering, natural language processing (NLP), experiments, and optimization.
• Hands-on experience with Google’s Customer Engagement Suite (CES) is required and non-negotiable, including building, configuring, and deploying solutions across CES components (e.g., Conversational Agents / Dialogflow CX, Agent Assist, Conversational Insights) for enterprise customer engagement use cases.
• Demonstrated experience building agentic AI systems in production – including single-agent and multi-agent architectures, planning and reasoning loops, tool/function calling, and orchestration with frameworks such as Vertex AI Agent Builder, ADK (Agent Development Kit), LangGraph, LangChain, CrewAI, or AutoGen.
• Proven ability to integrate new tools and external systems (REST/GraphQL APIs, internal microservices, databases, vector stores, knowledge bases, MCP servers) to extend agent capabilities, and to own deployment, CI/CD, monitoring, evaluation, and guardrails for agents running in production.
• Ability to apply  inference, frequentist statistics, causal modeling, and/or machine learning techniques.
• Experience with any of these: customer segmentation, A/B experiments, quasi-experiments, sales forecasting, churn propensity modeling, customer lifetime value analysis, credit risk, geospatial analytics, survey key-drivers, marketing mix modeling, multi-touch attribution, or recommender systems.

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