As a GenAI Product Builder, you’ll embed directly with Tempus teams — from health systems to life science organizations to internal operations— to help them identify real problems and solve them using agentic GenAI solutions.
Compensation
CHI - $110,000 - $140,000
Responsibilities
Partner with customer and business leaders to uncover high-impact use cases where GenAI can deliver measurable value
Through customer research, problem discovery and systems thinking, translate vague operational challenges into clear, functional solutions
Build rapid prototypes / proof-of-concepts to validate ideas and drive alignment
Design and implement agent-based solutions — from workflow automation to patient communication, insight generation, or clinical operations
Work directly with LLMs and agent frameworks (prompt design, chaining, context management, evaluation, and guardrails)
The Gemini Agent Platform AI Engineer will help accelerate the adoption of Google Cloud, Gemini and agent-based artificial intelligence technologies across Unilever. This is a hands-on engineering role with a strong product engagement dimension.
Responsibilities
Partner with product and engineering teams to understand existing applications, technical requirements and modernisation needs, and identify appropriate opportunities to adopt Google Cloud technologies.
Support the migration and modernisation of applications onto Google Cloud, working with product teams and architects to define target architectures and practical engineering approaches.
Design, develop and deploy artificial intelligence applications and intelligent agent solutions using Google Gemini and Google Cloud technologies.
Take solutions through the complete engineering lifecycle, from experimentation and technical design through development, testing, deployment, monitoring and continuous improvement.
Build reusable components, frameworks and engineering patterns that accelerate future artificial intelligence and Google Cloud implementations.
Develop and deploy advanced analytics, machine learning, and AI solutions that drive customer growth, retention, and portfolio optimization at scale.
Responsibilities
Independently design, train, tune, evaluate, deploy, and maintain machine learning models in production environments.
Lead the end-to-end lifecycle of machine learning models including problem definition, exploratory data analysis (EDA), feature engineering, validation, monitoring, and optimization.
Develop predictive models for attrition, cross-sell propensity, dormancy prediction, Next Best Action recommendations, portfolio compression, customer segmentation, and customer value optimization.
Design and deploy AI-powered solutions leveraging GenAI, LLMs, NLP, and call listening/conversational intelligence to identify customer sentiment, churn risk indicators, and growth opportunities.
Partner closely with cross-functional teams including Portfolio Management & Customer Success, Product, Sales, Marketing, Finance, BI, Data Engineering, and MLOps to source, validate, engineer, and operationalize data assets and model features.
We are looking for a technically skilled and client-focused Technical Consultant to support the implementation, configuration, and optimization of AI-powered compliance and financial crime solutions.
Responsibilities
Work closely with clients, business stakeholders, and compliance teams to gather requirements and onboard them onto the FinTech platform.
Provide accurate effort estimates for assigned work items and contribute to delivery planning by collaborating with TPMs and internal stakeholders.
Participate in client and internal workshops to gather requirements, align on solution design, facilitate implementation activities, and drive successful project delivery.
Translate functional and regulatory requirements into technical and configuration specifications.
Collaborate with Solution Architects and Engineering teams to design and implement scalable solutions.
Drive the next generation of AI-assisted silicon development, combining traditional RTL design and verification expertise with AI/ML technologies to improve engineering productivity, quality, and development efficiency across the silicon lifecycle.
Locations (2)
Taipei, Taiwan, Taiwan
Hsinchu, Taiwan, Taiwan
Responsibilities
Develop and maintain high-quality RTL in Verilog/SystemVerilog for ASIC/SoC products
Participate in architecture definition, microarchitecture development, implementation, and integration
Develop UVM-based verification environments, test plans, testcases, assertions, and coverage models
Debug functional, performance, and integration issues across IP, subsystem, and SoC levels
Develop AI agents and copilots to accelerate RTL design, verification, debug, code review, and productivity workflows
Requirements
BS or above degree in Electrical Engineering, Computer Engineering, Computer Science, or related field
Lead a team of technical architects and consultants as they deliver MuleSoft solutions to customers, driving Enterprise AI Architecture and building the technical practice.
Responsibilities
Lead a team of technical architects and consultants as they deliver MuleSoft solutions to customers
Drive Enterprise AI Architecture by designing and integrating scalable predictive and generative AI solutions within custom Salesforce and MuleSoft environments
Lead technical design sessions; architect and document complex integration solutions aligned with client business objectives
Champion an AI-first delivery methodology, utilizing advanced AI tools, developer assistants (e.g., Cursor, Claude), and agentic evaluation frameworks to accelerate API implementation and integration lifecycle velocity across projects
Provide hands-on delivery guidance to influence standards for development, governance, and the operational life cycle
Requirements
Bachelor degree in computer science or equivalent
Masters
Nice to have: Continuous Integration (CI) pipelines
Nice to have: Publish & subscribe integration design models
Help design, build, and deploy SAP GenAI and Agentic AI solutions that support finance technology and core ERP transformation work within the Advisory practice.
Responsibilities
Designing and developing GenAI and Agentic AI solutions for SAP and enterprise use cases with production readiness in mind
Building RAG-based applications using SAP data, technical documentation, and knowledge repositories
Creating AI accelerators for SAP code analysis, modernization, test generation, data mapping, impact analysis, and knowledge assistants
Integrating LLM solutions with SAP applications, APIs, services, and enterprise data sources
Developing graph-based memory, tool-using agents, and context-aware AI components for multi-agent workflows
Requirements
Bachelor's degree
Nice to have: SAP S/4HANA System Administration
Nice to have: SAP Business Technology Platform (BTP)
Lead advanced analytics, data science, and AI initiatives to drive commercial performance insights across multiple markets and business units.
Responsibilities
Translate leadership questions into analytics and AI solutions
Own performance-analytics foundation and data governance
Build and improve dashboards and analytics products
Develop predictive and diagnostic models
Design and deploy AI-enabled tools and conversational agents
Requirements
Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, Statistics, Econometrics, Engineering, Mathematics or another quantitative discipline
AI Engineer focusing on leveraging advanced technologies and techniques to design and develop robust data solutions for clients, specifically in intelligent automation, process mining, and implementing intelligent process automation and robotic process automation.
Responsibilities
Design, develop, and deploy GenAI solutions, with a focus on real-world production readiness.
Build and scale Retrieval-Augmented Generation (RAG) systems for enterprise use cases.
Integrate with graph-based memory and tool-using agents to enhance LLM capabilities.
Implement multi-agent orchestration and reasoning systems (e.g., AutoGen, LangGraph, custom agents).
Use LLM frameworks and libraries (e.g., LangChain, AutoGen, LangGraph etc).
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