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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Principal Data and AI Product Engineer - **Company:** MetLife - **Location:** New York, NY, United States - **Experience:** Experienced - **Salary:** $164,600.0 - $213,100.0 - **Contract:** Permanent contract - **Skills:** Training Data, Application Programming Interfaces (APIs), Artificial Intelligence, Audit Trail, Cloud Database, Continuous Integration, Information Engineering, Data Infrastructure, Python (Programming Language), Machine Learning, Software Product Management, SQL Databases, Data Streaming, Systems Integration, Cloud Platform System, Retrieval-Augmented Generation, SHAP (Shapley Additive Explanations), Model Validation, Agentic-AI, Data Strategy, Information Technology, Production Code, Machine Learning Operations, Data Pipelines - **Published:** October 1, 2026 - **Apply:** https://www.manhattanjobs.com/job.asp?id=3413461110&tx=DT10394UYZ&pt=1&aff=0B19D771-A501-4A5E-8338-2A822B784D54&utm_source=Job%20Feed&utm_medium=textkernel&utm_campaign=DE&utm_term=0B19D771-A501-4A5E-8338-2A822B784D54 ## About the Role * Bachelor's degree in Computer Science, Engineering, Mathematics, Statistics, or a related quantitative field. * 5+ years of experience in data engineering, data science/AI, and data strategy, including 3+ years leading the technical direction of a platform, product, or capability. * Proven track record delivering and operating production-grade data or AI solutions. * Experience designing data and AI architectures, evaluating and building platforms/vendors, and making build vs. buy decisions. * Strong hands-on skills in Python and SQL, with experience reviewing production code and validating models. * Experience with machine learning and generative AI, including RAG and agent-based architectures. * Deep understanding of modern data platforms, data pipelines, APIs, integrations, and cloud-based data architectures., * Advanced degree in Computer Science, Engineering, Statistics, Mathematics, or a related quantitative field. * Regulated-environment platform experience: insurance, financial services, or another regulated industry, including platform security (access controls, PII handling, encryption, audit logging), infrastructure-as-code, and cloud cost management. * MLOps and model governance: experiment tracking, CI/CD for models, inference patterns, drift monitoring, explainability (SHAP or equivalent), and model-risk readiness. * Semantic layer and AI-ready data: semantic layer design and metric governance, data contracts, catalog and lineage, making enterprise data consumable by models and agents. * Leadership and influence: holds a technical position with senior stakeholders and vendors and revises it on evidence rather than seniority; explains technical trade-offs to executive audiences without diluting them and influences across a matrixed organization without formal authority; builds durable credibility with senior executives, becoming the person leadership consults before committing to a data or AI direction; operates without a defined brief, deciding what the organization should work on and not only how to build it; raises the technical bar for peers through review, mentoring, and the architecture patterns they set. ## Description The Data & AI Product Engineer is a hands-on technologist who sets the technical direction for how MetLife builds and consumes data and AI capabilities. The role defines what the enterprise needs, architects the target state from a blank page, and drives it through to production. It spans data science, data engineering and data strategy. This is a forward-deployed role, not a portfolio or project management role. It requires the ability to take an ambiguous, high-stakes problem spanning multiple systems, owners, and constraints and reduce it to a defensible architecture and a sequenced delivery plan, then build against it hands-on. It also requires the credibility to bring that recommendation directly to executive leadership and change the decision on what MetLife should build, what it should not, and how data and AI capabilities integrate with the broader IT landscape., * Work with domain stakeholders to identify capability needs, assess feasibility early, and translate problems into implementable requirements. * Help shape the technical direction for MetLife's data and AI landscape, defining the target state, the build-versus-buy position, and the sequence of investments required to get there, and keeping that direction current as the platform and vendor market shifts. * Take on the large and ambiguous cross-cutting initiatives, such as semantic layer enablement and preparing enterprise data for frontier AI models, and bring structure to them: frame the problem, establish the decision criteria, resolve competing technical positions on evidence, and carry the work from concept through enterprise adoption. * Architect solutions from a blank page: target-state architectures, data flow and integration diagrams, and component models precise enough to build from directly. * Design and deliver hands-on solutions across data science and data engineering. * Review design and code from concept through production and own the production-readiness decision. * Guide enterprise initiatives toward the right capability, ensuring teams build on approved platforms and existing solutions rather than duplicating them, and steering misaligned approaches early. * Serve as the trusted technical voice for data and AI with senior executive stakeholders, including the CDO, CIO, and their leadership teams. Frame trade-offs and recommendations so non-technical executives can decide with confidence, and secure the alignment and investment required to move a direction forward. ## Related Videos - [Developer Experience, Platform Engineering and AI powered Apps](https://www.wearedevelopers.com/videos/990-developer-experience-platform-engineering-and-ai-powered-apps) - [Fireside Chat: Deep Learning, Deep Impact: Harnessing AI for Language Innovation](https://www.wearedevelopers.com/videos/612-fireside-chat-deep-learning-deep-impact-harnessing-ai-for-language-innovation) - [Resilient by Design: Building Robust Architectures in High-Stakes Financial Systems](https://www.wearedevelopers.com/videos/2106-resilient-by-design-building-robust-architectures-in-high-stakes-financial-systems) - [Data: The Deciding Factor in AI Success](https://www.wearedevelopers.com/videos/100310-data-the-deciding-factor-in-ai-success) - [Exploring 5 Key Applications of AI Abundance with Blockchain Assurance](https://www.wearedevelopers.com/videos/971-exploring-5-key-applications-of-ai-abundance-with-blockchain-assurance) - [No Keys for the Robot: GitOps as the Control Plane for Autonomous Agents](https://www.wearedevelopers.com/videos/100095-no-keys-for-the-robot-gitops-as-the-control-plane-for-autonomous-agents) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift)