Senior/Lead Product Manager - Core AI Platform at Interface AI

San José del Valle, Andalusia, Spain -

Full Time

Start Date

Immediate

Expiry Date

16 Mar, 26

Salary

240000.0

Posted On

16 Dec, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Product Management, AI/ML, Technical Fluency, Platform Interfaces, Cross-Functional Execution, Enterprise SaaS, Model Lifecycle, Inference Performance, Compliance, Safety Systems, Voice Systems, Real-Time Constraints, Multi-Agent Orchestration, Evaluation Frameworks, Prompt Optimization, Data Handling

Industry

Software Development

Description

Banking is being reimagined—and customers expect every interaction to be easy, personal, and instant. We are building a universal banking assistant that millions of U.S. consumers can use to transact across all financial institutions and, over time, autonomously drive their financial goals. Powered by our proprietary BankGPT platform, this assistant is positioned to displace age-old legacy systems within financial institutions and own the end-to-end CX stack, unlocking a $200B opportunity and potentially replacing multiple publicly traded companies. Ultimately, our mission is to drive financial well-being for millions of consumers. With over two-thirds of Americans living paycheck to paycheck, 50% holding less than $500 in savings, and only 17% financially literate, we aim to put financial well-being on autopilot to help solve this problem. About the Role As a Senior/Lead Product Manager – Core AI Platform, you will own the vision, roadmap, and execution for the Core Agentic AI Platform that powers all interface.ai products. This is a foundational, deeply technical role. You will define the platform primitives that enable: Core agentic behavior (planning, goal routing, memory, context switching, tool use) Safe and compliant AI operation in regulated environments (PII controls, auditability, policy enforcement) Scalable, low-latency inference and multi-model orchestration across voice and chat experiences Continuous model iteration (evaluation, benchmarking, prompt/model optimization loops) Expansion beyond a single vertical by building reusable, configurable platform capabilities You will partner tightly with Core AI Engineering, Research, Product Engineering, Design, and GTM/Delivery teams to turn platform capabilities into measurable product outcomes. Key Responsibilities Define the Core AI Platform Vision and Roadmap Set platform strategy for the agent runtime layer: multi-agent orchestration, memory/context, tool routing, and policy-aligned behavior. Prioritize platform investments that scale across product lines and enable future vertical expansion. Define clear platform contracts so product teams can reliably build on the platform. Own the Model Lifecycle and Model Evolution Product Surface Drive the roadmap for model selection, evaluation, fine-tuning enablement, and benchmarking. Partner with engineering to define workflows and requirements for fine-tuning pipelines, dataset strategy, and safe experimentation. Establish decision frameworks for when to prompt-tune vs fine-tune vs switch models, balancing quality, latency, and cost. Inference Performance, Reliability, and Cost Define product requirements for high-throughput, low-latency inference and runtime efficiency (caching, batching, quantization strategy, token efficiency). Establish reliability patterns: multi-region deployments, fallbacks, graceful degradation, and safe rollouts (flags/canaries/rollback). Build cost/latency governance: budgets, monitoring, and optimization priorities across high-scale deployments. Safety, Guardrails, and Compliance by Design Own platform-level requirements for automated PII detection/masking, prompt/response safety policies, and data handling controls. Drive secure-by-default platform capabilities: tenant isolation, encryption expectations, retention controls, audit logs, and access control requirements. Ensure the platform can support compliance needs (e.g., SOC2/GDPR readiness) through measurable controls and operational rigor. Evaluation Harnesses and Production Quality Loops Establish the eval strategy and roadmap: offline golden sets, regression testing, online quality metrics, and automated safety checks. Define how teams measure factual accuracy, hallucination risk, task success, latency, and cost efficiency—then make it actionable via tooling and dashboards. Create feedback loops from production to improve prompts/models/policies continuously. Voice / Speech-to-Speech and Multimodal Enablement Drive platform requirements for real-time conversational intelligence: ASR/TTS integration patterns, latency budgets, and quality metrics (WER, interruption handling, turn-taking). Prioritize multimodal platform primitives that improve naturalness, responsiveness, and user trust in voice experiences. Cross-Team Alignment and Adoption Partner with PMs and engineering leads across product lines to drive platform adoption, migration plans, and deprecation/versioning strategy. Translate deep technical constraints into clear product trade-offs and execution plans. Maintain crisp documentation, onboarding paths, and operating rhythms for platform consumers. Required Qualification 5+ years product management experience, ideally on platform, AI/ML, infra, or developer-facing products. Strong technical fluency: able to write product specs for model lifecycle, inference/runtime, evals, and safety systems; comfortable partnering daily with senior/staff engineers. Experience defining platform interfaces and driving adoption across multiple product teams (APIs, versioning, migration strategy). Proven ability to lead cross-functional execution with measurable outcomes (metrics, dashboards, experiments). Experience building in enterprise SaaS environments with multi-tenant requirements, governance, and operational rigor. Preferred Qualification Experience with LLM systems, multi-agent orchestration, and evaluation frameworks. Familiarity with fine-tuning, RLHF/RLAIF concepts, and prompt optimization loops (as product domains). Experience with voice/ASR/TTS systems and real-time latency-sensitive product constraints. Exposure to regulated domains (fintech, healthcare, insurance) and compliance-driven product requirements. Competitive salary, bonus, and equity. (Compensation may vary based on skills and location.). Base Salary Range 200-240k Benefits 💡 100% paid health, dental & vision care 💰 401(k) match & financial wellness perks 🌴 Discretionary PTO + paid parental leave 🏡 Remote-first flexibility 🧠 Mental health, wellness & family benefits 🚀 A mission-driven team shaping the future of banking At interface.ai, we are committed to providing an inclusive and welcoming environment for all employees and applicants. We celebrate diversity and believe it is critical to our success as a company. We do not discriminate on the basis of race, color, religion, national origin, age, sex, gender identity, gender expression, sexual orientation, marital status, veteran status, disability status, or any other legally protected status. All employment decisions at Interface.ai are based on business needs, job requirements, and individual qualifications. We strive to create a culture that values and respects each person's unique perspective and contributions. We encourage all qualified individuals to apply for employment opportunities with Interface.ai and are committed to ensuring that our hiring process is inclusive and accessible.

Responsibilities

Define the vision and roadmap for the Core AI Platform, focusing on agentic behavior and compliance in regulated environments. Partner with engineering and product teams to ensure platform capabilities translate into measurable product outcomes.