Lead the design and implementation of AI-driven enterprise applications, architecting next-generation generative AI solutions for Go-To-Market and customer experience processes.
Compensation
$223,000.00 - $306,500.00/yr
Responsibilities
Architect AI Gateway and unified API for LLMs
Design and implement observability pipelines for GenAI
Optimize cost and performance of LLM usage
Integrate guardrails for safety and compliance
Lead multi-agent system development
Requirements
Bachelor's degree in Computer Science or related field
Lead hands‑on architecture, design, and implementation of agentic applications, AI‑powered services, and platform capabilities from concept through production
Define engineering patterns and best practices for production AI systems, including evaluation, monitoring, guardrails, resiliency, cost control, and rollback strategies
Drive end‑to‑end software delivery across the SDLC, from discovery and prototyping to testing, release, and production operations
Use engineering tools to accelerate design, coding, testing, documentation, troubleshooting, and delivery while maintaining strong engineering judgment and code quality standards
Champion an AI‑enabled SDLC by improving developer workflows, automation, test generation, code review quality, release confidence, and team productivity
Requirements
Bachelor’s degree in computer science, Software Engineering, or a related technical field
Lead the design, development and operationalisation of GenAI models, intelligent agents, copilots and automation solutions across Bupa.
Responsibilities
Lead the design, development and operationalisation of GenAI models, intelligent agents, copilots and automation solutions across Bupa.
Own AI solution delivery end-to-end, from discovery and scoping through to evaluation, build, testing, deployment, monitoring and optimisation.
Oversee technical solution design, integrations, APIs, data flows and model selection, ensuring alignment with Bupa’s AI and architecture standards.
Manage key elements of the AI engineering stack, including LLM platforms, orchestration frameworks, vector databases, retrieval pipelines, security controls and cloud infrastructure.
Partner with business teams to understand their needs and ensure AI solutions deliver measurable value and improved operational outcomes.
AI Outcome Customer Engineer supporting enterprise AI solutions on Google Cloud, bridging presales and post‑sales, leading technical delivery and orchestration.
Responsibilities
Lead technical delivery strategy and orchestration
Debug systems and collaborate with product and engineering teams
Design and integrate AI/ML models, APIs, ECM, ID, and cloud infrastructure
Act as an enterprise architect, technical debugger, and engineering liaison for AI solutions, bridging pre‑sales and post‑sales execution.
Responsibilities
Partner with Account teams and practice Customer Engineers during technical evaluation phases to assess project feasibility, shape proposals for long‑term adoption, and validate FDE engagement requests.
Lead upfront technical design for enterprise‑grade AI solutions, ensuring seamless and secure integration of models, agents, and connectors into existing customer data pipelines, identity providers, and compliance boundaries.
Dive into code‑level context to diagnose and resolve complex customer implementation issues, identify core product bugs, and test workarounds to clear execution roadblocks.
Serve as the definitive liaison to core Product and Engineering teams, troubleshooting systemic deployment blockers and translating real‑world field feedback into actionable feature requests.
Steer implementation strategy through technical authority and architectural foresight while owning the technical reality of delivery alongside customer‑facing teams.
Requirements
Bachelor's degree
Nice to have: orchestrating specialized technical resources
Nice to have: presenting to executive leaders
Nice to have: deep technical problem solving
Nice to have: executive communication
Skills
I
Automation Test Engineer (Playwright, Java, GenAI)
Perform functional and automation testing for Capital Markets, develop and maintain automation frameworks, validate data, and leverage GenAI technologies to improve test design and automation.
Responsibilities
Perform functional and automation testing for Capital Markets ensuring quality, reliability, and performance.
Understand index methodologies, benchmark indices, and capital markets data, validating index calculations and financial applications.
Develop and maintain automation frameworks using Playwright/TypeScript, Selenium/Java and execute regression, API, and end-to-end testing.
Validate data using SQL, databases, ETL pipelines, and Data Services to ensure data accuracy and integrity.
Demonstrate AI fluency by leveraging AI-powered tools and Agentic AI/GenAI assistants to improve test design, test automation, defect analysis, root cause investigation, and overall QE productivity.
Director, Applied AI Product ManagerBNY (The Bank of New York Mellon Corporation) · New York, US
Director & Principal Engineer, AI/ML & MLOps PlatformRoyal Bank of Canada · Vancouver, Canada
Finance AI Transformation SpecialistVodafone · Chalandri, Greece
Lead Software Engineer (Full stack)Mastercard · Mexico City, Mexico
Senior Technology Manager - AI HubBupa · Melbourne, Australia
AI アウトカム カスタマー エンジニア、AI GTM、Google Cloud（英語、日本語）Google · Tokyo, Japan