Senior Machine Learning Engineer

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.

• Consult with stakeholders to gather business requirements translate them into agentic AI and data solutions design high-level agent and model architectures 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 Googles 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.

• Degree in a quantitative discipline such as Data Science Applied Mathematics Statistics Economics Operations Research Computer Science Mathematics Physics Biology Chemistry or Engineering. An advanced degree Data Science bootcamp or MOOC certification is a plus
• 3-5 years of work experience in classification regression clustering natural language processing (NLP) experiments and optimization
• Hands-on experience with Googles 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 Bayesian 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
• Highly skilled in R and Python for statistical and machine learning programming
• Highly skilled in SQL & Python coding to wrangle and explore structured & unstructured data
• Proficient with server or Cloud computing platforms such as Google Compute Engine or EC2
• Proficient with data warehouses such as Oracle BigQuery or AWS
• Subject matter scientist that can review the literature to identify state-of-the-art solutions to a business problem

Preferred Qualifications

• Google Cloud certifications (e.g. Professional Machine Learning Engineer Professional Cloud Architect or Generative AI Leader) and demonstrated specialization in Googles CES and Vertex AI ecosystems
• Experience with Gemini models function calling structured outputs prompt engineering prompt evaluation and fine-tuning / parameter-efficient tuning of foundation models on Vertex AI
• Experience designing Retrieval-Augmented Generation (RAG) pipelines with vector databases (e.g. Vertex AI Vector Search Pinecone Weaviate pgvector) and grounding agents on enterprise knowledge
• Experience with AgentOps and LLMOps tooling - tracing evaluation harnesses online/offline evals red-teaming prompt and tool versioning cost and latency observability
• Experience implementing responsible AI practices for agents - safety PII handling hallucination mitigation human-in-the-loop review and policy/guardrail enforcement
• Experience integrating agents with enterprise systems such as CRM (Salesforce) CCaaS / contact center platforms billing ticketing and identity providers in a regulated environment
• Experience with containerization and orchestration (Docker Kubernetes / GKE Cloud Run) and infrastructure-as-code (Terraform) for deploying agentic services
• Contributions to open-source agentic AI projects publications patents or conference talks in the GenAI / agentic AI space