Job description

Are you looking to Optimize your life? Start your exciting path to a rewarding career today! We are Optimum, a leader in the fast-paced world of connectivity, and we're seeking driven and enthusiastic professionals to join our team, empower lives, fuel businesses, and drive innovation. Connectivity is no longer a luxury, but a necessity. A career at Optimum means you'll be enabling progress and enhancing lives by providing reliable, high-speed connectivity solutions that keep the world connected. Our successes, now and in the future, are powered by our amazing product, a commitment to our people and culture, and the connections we make in our communities. If you are resourceful, collaborative, and passionate about delivering consistent excellence, Optimum is for you! Job Summary 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 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 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 • 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 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 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 Google's 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 At Optimum, every action and interaction we take part in, is driven by our three Guiding Principles: Do What’s Right, Drive One Optimum, and Make It Happen. These aren’t just words, they help us build trust, create real community, and embrace new ways of thinking. Our employees are empowered to do the right thing for our customers and co-workers and to recognize and reward these behaviors when we see them. It’s all part of the bigger picture of “Be The Difference” where each employee knows they have the power to enact real change, share new ideas, and understand that learning never stop. If you have the drive to succeed and are ready to embark on a thrilling career, seize this opportunity today, and join our winning team. Together, we'll shape the future of connectivity. All job descriptions and required skills, qualifications and responsibilities for a particular position are subject to modification by the Company from time to time, in the Company’s discretion based on business necessity. We are an Equal Opportunity Employer committed to recruiting, hiring and promoting qualified people of all backgrounds regardless of gender, race, color, creed, national origin, religion, age, marital status, pregnancy, physical or mental disability, sexual orientation, gender identity, military or veteran status, or any other basis protected by federal, state, or local law. The Company collects personal information about its applicants for employment that may include personal identifiers, professional or employment related information, photos, education information and/or protected classifications under federal and state law. This information is collected for employment purposes, including identification, work authorization, FCRA-compliant background screening, human resource administration and compliance with federal, state and local law. Applicants for employment with The Company will never be asked to provide money (even if reimbursable) as part of the job application or hiring process. Please review our Fraud FAQ for further details. Pay is competitive and based on a number of job-related factors, including skills and experience. The starting pay rate/range at time of hire for this position in the posted location is $156,774.00 - $198,273.00 / year. The rate/range provided herein is the anticipated pay at the time of hire and does not reflect future job opportunity.

Requirements

Must-haves

Are you looking to Optimize your life? Start your exciting path to a rewarding career today!
If you are resourceful, collaborative, and passionate about delivering consistent excellence, Optimum is for you!
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.
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.
3-5 years of work experience in classification, regression, clustering, natural language processing (NLP), experiments, and optimization
Ability to apply Bayesian inference, frequentist statistics, causal modeling, and/or machine learning techniques
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

Nice-to-haves

Google Cloud certifications (e.g., Professional Machine Learning Engineer, Professional Cloud Architect, or Generative AI Leader) and demonstrated specialization in Google's 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
If you have the drive to succeed and are ready to embark on a thrilling career, seize this opportunity today, and join our winning team. Together, we'll shape the future of connectivity.
Applicants for employment with The Company will never be asked to provide money (even if reimbursable) as part of the job application or hiring process. Please review our Fraud FAQ for further details.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

machine learningaimlrestgraphqlci/cdpythonsqlawsdockerkubernetes

Platform: successfactors
Effort: medium