Prudential’s purpose is to be partners for every life and protectors for every future. Our purpose encourages everything we do by creating a culture in which diversity is celebrated and inclusion assured, for our people, customers, and partners. We provide a platform for our people to do their best work and make an impact to the business, and we support our people’s career ambitions. We pledge to make Prudential a place where you can Connect, Grow, and Succeed. We are seeking a highly skilled and motivated AI Engineer to join our team. The ideal candidate will have a strong background in Python programming and experience with common machine learning frameworks such as XGBoost, KMeans clustering, and others. Knowledge of Big Query ML (BQML) and Vertex AI is a plus.
About the Role:
Join our global insurance leader as an AI Technical Product Management. You will bridge product strategy and engineering execution for our agentic AI platform by translating technology capabilities and constraints into clear business value. You will work closely with Engineering teams and business users to align requirements, decisions, and delivery. This role emphasizes strong technical knowledge, excellent communication, and the ability to decompose complex initiatives into high‑level engineering tasks and milestones. Engineering background is a plus.
Current Context: Product Strategy & Roadmap: Define vision, outcomes, KPIs/OKRs, and prioritized roadmaps for agentic AI use cases in regulated environments; ensure alignment with AI governance, PII masking, guardrails, and audit requirements. Technical Requirements & PRDs: Author clear PRDs/specs capturing user journeys, system boundaries, non‑functional needs (reliability, latency, observability), and compliance controls; convert business objectives into testable acceptance criteria. Engineering Task Breakdown: Decompose scope into epics, milestones, and dependency plans (protocol enablement, environment readiness, Ia C approvals, observability hooks) for execution by engineering. Cross‑Functional Delivery: Coordinate Data/AI Engineering, Platform, Security, and PMO; sequence dependencies (data pipelines, model services, gateways), remove blockers, and drive release readiness. Stakeholder Communication: Lead updates with business users and executives; articulate tradeoffs, risks, and decisions; maintain alignment across LBUs and governance bodies. Process Improvement: Continuously refining product and delivery processes (requirements intake, prioritization, release gates), establishing templates, RACI, and operating rhythms. Observability & Evaluation Oversight: Champion instrumentation, evaluation criteria, and post‑launch measurement to accelerate iteration (defined by engineering; owned by product). Vendor/Build Decisions: Assess partner solutions vs. internal build; synthesize technical constraints, TCO, and compliance considerations for decision making.
Job Responsibilities: Develop and implement machine learning models using Python or BQML.
Apply data preprocessing techniques such as one-hot encoding, label encoding, and data normalization to prepare datasets for analysis.
Collaborate with cross-functional teams to understand business needs and provide AI solutions.
Optimize existing machine learning pipelines for performance and scalability.
Stay up-to-date with the latest developments in AI and machine learning technologies.
Contribute to the development of internal tools and frameworks to streamline AI processes.
(Optional) Utilize BQML and Vertex AI to enhance model capabilities and deployment.
Job Qualification: Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
Possess 5 years or above Technical Product Management experiences.
Proven experience with Python programming and machine learning frameworks.
Familiarity with data preprocessing techniques such as one-hot encoding and label encoding.
Strong analytical and problem-solving skills.
Excellent communication and teamwork abilities.
(Preferred) Experience with BQML and Vertex AI.
Skills & Mindset: Passion & Continuous Learning: Embraces new technologies, is enthusiastic about AI, and commits to ongoing learning. Process Optimizer: Willing to continuously improve workflows and reduce friction. Open Communicator: Proactive in communication, receptive to feedback, and maintains an open mindset with stakeholders. Impact‑Driven: Prioritizes initiatives with the highest business and technical leverage; communicates value crisply. Collaborative & Global: Thrives in cross‑country, multi‑disciplinary settings; aligns partners and vendors to shared outcomes. Clear, Bilingual Communication: Excellent written/spoken English & Chinese (Mandarin) to engage both business and engineering audiences.
Technical Knowledge Required: Multi‑Agent orchestration patterns, Context/Memory (RAG), Agent gateway/guardrails, model evaluation basics. Understand the fundamentals of machine learning; know typical data requirements, model lifecycle, evaluation metrics, and common constraints to inform product decisions. Hybrid retrieval/grounding, systems/architectural thinking, and clean‑architecture principles for decision making. Data integration & governance: Knowledge of how AI training/serving data is sourced, integrated, composed, and operationalized across pipelines; understands policies, lineage, quality controls, and compliance implications in regulated environments.
Tech Stacks: Compute & Hosting: GKE & GCE (Red Hat), GCP Vertex AIAgentic Framework: ADK, n8n, Copilotkit, etc.Agentic Protocols: MCP, A2 A, AG-UI, A2 UIDatabases: Redis, Firestore, Postgre SQL, QdrantData Technologies: GCP Cloud Composer (Airflow), Big Query, Kafka Ecosystem (Confluent Cloud, Debezium, Qlik), Informatica, Power BIMonitoring & Observability: Langfuse, LGTM, GCP Monitoring, OTelCI/CD: Git Hub ActionsInfrastructure as Code: TerraformSecurity: VPC SC & Policy Tags, CMEK (KEK/DEK), VaultContainers: Docker, KubernetesAI Tools for Development: Gemini CLI, Github Copilot
What You Can Expect: Global Impact: Work in a cross-country, multi-lingual environment, collaborating with teams across the globe. Massive Scale: Contribute to solutions that impact a large volume of customers. Strategic Focus: Be at the forefront of a company that strategically focuses on AI and data to drive significant value and impact. Cross-Functional Collaboration: Work closely with first-line business users, gaining direct insights into their needs and shaping technology solutions that address real-world challenges. Meaningful Technology: Make technology truly impactful, driving innovation and solving critical business problems. Role Briefly: Product Strategy & PRD, Engineering Decomposition (High‑Level), Multi‑Agent/Context Knowledge, Responsible AI & Compliance, Process Optimization, Hybrid Cloud Awareness. Expectations for Three Months: Establish initial product vision, PRDs, and a cross‑team delivery plan; stand up governance and measurement baselines aligned to platform standards. Expectations Within One Year: Launch one or more high‑impact AI capabilities to production with measurable value and reliability; institutionalize product operating rhythms (roadmaps, reviews, post‑launch metrics) across teams. Specific contributions can be discussed.
