机器学习/人工智能平台工程师负责人（欧洲、欧洲中部和东欧远程）
Lead ML/AI Platform Engineer (EU, EMEA Remote)
SavvyMoney 是一家位于美国的领先金融科技公司。我们为全美 1,600 多家银行和信用合作社合作伙伴提供集成的信用评分和个人理财解决方案。SavvyMoney 解决方案已与超过 43 个数字银行平台集成。
SavvyMoney 最近被旧金山商业时报和硅谷期刊评为“旧金山湾区最佳 25 家工作场所”之一，并被评为 Inc. 5000 最快成长公司。
我们公司正在快速发展，正在寻找独立的**高级数据工程师承包商**来支持增长。
该独立承包商将从波兰华沙的家中远程工作，作为美国、加拿大、欧洲以及印度多个地点的分布式团队成员。
我们正在扩大团队，正在寻找一名**机器学习/AI 平台负责人工程师**。
### 职位描述
#### 概述
在 SavvyMoney 担任机器学习/AI 平台负责人工程师，您将负责将机器学习从实验阶段推向生产环境的平台——包括训练基础设施、模型服务、推理流水线以及与我们的 Java 微服务的集成接口。
您将与我们的数据平台架构师一起，为公司范围内的 AI/ML 设定技术方向，并推动我们的生成式 AI/大语言模型和代理策略的工程方面——从检索架构和评估工具到在受监管环境中负责任地运行代理工作流所需的保障机制。
### 您将负责
- 与我们的数据平台架构师合作，为公司范围内的 AI/ML 设定技术方向——包括架构、工具、标准以及自建与采购决策。
- 负责 ML/AI 平台：训练基础设施、模型服务、推理流水线和生产集成。
- 在 Amazon SageMaker 中进行特征工程、模型训练、模型注册和托管推理
- 在 Amazon Bedrock 和 AgentCore 中使用生成式 AI/大语言模型、微调和代理工作流
- 基于 AWS Glue、Lambda 和 Step Functions 构建反馈和数据流水线
- 负责服务层，并将 ML 服务与我们的 Java 微服务干净地集成——定义 API 协议并做出延迟与吞吐量的权衡。
- 推动我们生成式 AI/大语言模型策略的工程方面：检索架构、评估工具、服务模式，以及针对不同问题选择合适方法的判断。
- 在新兴代理栈方面提供深度——MCP、代理工作流模式、无状态和有状态代理等。
查看英文原文
**SavvyMoney** is a US based leading financial technology company. We provide integrated credit score and personal finance solutions to 1,600 + bank and credit union partners throughout the United States. The SavvyMoney solutions integrate with more than 43 digital banking platforms.
**SavvyMoney was recently recognized by the San Francisco Business Times and the Silicon Valley Journal as one of the "Top 25 Places to Work in the San Francisco Bay Area" and is an Inc. 5000 Fastest Growing Company.**
Our company is growing and we are looking for Independent **Senior Data Engineer Contractors** to help support the growth.
****This Independent Contractor will work 100% Remotely from your home office in Warsaw, Poland as part of a distributed team in the USA, Canada, Europe and several locations in India.**
We are growing our team and looking for a **Lead ML/AI Platform Engineer**.
### Job Description
#### Overview
As a Lead ML/AI Platform Engineer at SavvyMoney, you will own the platform that takes machine learning from experiment to production — training infrastructure, model serving, inference pipelines, and the integration seams with our Java microservices.
You will set technical direction for AI/ML across the company alongside our Data Platform Architect, and drive the engineering side of our GenAI/LLM and agent strategy — from retrieval architectures and evaluation harnesses to the guardrails required to run agentic workflows responsibly in a regulated environment.
### What You'll Do
- Partner with our Data Platform Architect to set technical direction for AI/ML across the company — architecture, tooling, standards, and build-vs-buy decisions.
- Own the ML/AI platform: training infrastructure, model serving, inference pipelines, and production integration.
- Feature engineering, model training, model registry, and hosted inference in Amazon SageMaker
- GenAI/LLM usage, fine-tuning, and agentic workflows in Amazon Bedrock and AgentCore
- Feedback and data pipelines built on AWS Glue, Lambda, and Step Functions
- Own the serving layer and integrate ML services cleanly with our Java microservices — define the API contracts and make the latency and throughput trade-offs.
- Drive the engineering side of our GenAI/LLM strategy: retrieval architectures, evaluation harnesses, serving patterns, and the judgment calls about which approach fits which problem.
- Bring depth on the emerging agent stack — MCP, agent workflow patterns, stateless and stateful designs, and the guardrails needed to run them responsibly in a regulated environment.
- Partner with our Data Scientist on the handoff from experimentation to production: productionize models, stand up the feature pipelines and serving infrastructure they need, and shorten the loop between training and deployment.
- Work with product, data, and engineering leadership to identify the highest-impact ML opportunities and translate them into roadmaps.
- Represent the AI/ML function in cross-functional forums, communicating trade-offs clearly to technical and non-technical audiences alike.
### What We're Looking For
**Required**
- 8+ years in software or ML engineering, including 5+ years shipping production ML systems and a track record of owning ambiguous, high-scope problems end to end.
- Demonstrated technical leadership: you've shaped the ML strategy of a team or organization, mentored senior engineers, and been the person others rely on for difficult architectural calls.
- Hands-on experience with both operating models we use:
- **AWS managed ML stack:** Amazon SageMaker (training, tuning, hosted endpoints, model registry), Amazon Bedrock, and AgentCore for GenAI and agentic workflows.
- **Open-source ML tooling:** JupyterLab for notebooks, Spark for distributed processing, MLflow for experiment tracking and model registry.
- Deep working knowledge of the AWS stack — S3, Athena, Redshift, Glue, Step Functions, Lambda — plus SQL skills strong enough to model data for both analytical and ML workloads.
- Production experience with GenAI/LLMs: RAG, prompt engineering, evaluation, and a clear grasp of the cost, latency, and safety trade-offs involved.
- Familiarity with vector databases (e.g., pgvector, Pinecone) and sound judgment on when they're warranted versus alternatives such as NoSQL retrieval.
- Working knowledge of Java sufficient to review service code, define API contracts, and debug integration issues with our microservices.
- Deep expertise in Python and the core ML stack: scikit-learn, pandas, NumPy, PyTorch and/or TensorFlow, XGBoost / LightGBM.
- Solid MLOps fundamentals — model monitoring, drift detection, reproducibility, experiment tracking, model registry, and cost observability — plus the ability to partner with DevOps on CI/CD rather than build it from scratch.
- Excellent written and verbal communication — you can write both the design doc that aligns a dozen engineers and the one-pager that aligns the exec team.
- Strong collaborator, comfortable operating in a role where scope is shared: you'll partner with a Data Scientist on models and DevOps on infrastructure, and you can navigate those seams while keeping clear ownership.
- Ability to operate as an independent contractor through your own entity or an approved contracting arrangement, with reliable overlap with US Pacific business hours for architecture reviews and cross-team work.
**Nice to Have**
- Experience with ClickHouse or a comparable columnar / real-time analytical database.
- Fine-tuning experience (LoRA / QLoRA, instruction tuning, or RLHF).
- Streaming and real-time inference experience (Kafka, Kinesis, low-latency serving).
- Infrastructure-as-code (Terraform, AWS CDK, CloudFormation).
- Experience operating ML systems at meaningful scale — hundreds of millions of predictions per day, or equivalent.
- Open-source contributions, conference talks, papers, or patents in ML / applied ML.
### Who You'll Work With
- **Data Scientist** — leads feature engineering, model training, fine-tuning, and experimentation. You'll partner on the research-to-production handoff, stand up the infrastructure they need, and jointly own the quality of what ships.
- **DevOps** — owns CI/CD, deployment infrastructure, and platform observability. You'll partner on ML-specific extensions (model artifacts, reproducible environments, canary and shadow deployments) rather than rebuilding what already exists.
- **Backend engineers** — own the Java microservices that consume ML outputs. You'll define the contracts and own the serving side.
- **Product and engineering leadership** — you're the technical voice on what we should build with AI/ML, and how.
### Our Stack
- **Distributed processing:** AWS Glue
- **Orchestration:** AWS Lambda, AWS Step Functions
- **Managed ML:** Amazon SageMaker, Amazon Bedrock, AgentCore
- **Open-source ML:** JupyterLab, Spark, MLflow
- **Storage & query:** Amazon S3 (data lake), Amazon Athena, Amazon Redshift, ClickHouse
- **Languages:** Python (ML, pipelines), Java (microservices)
**Why This Role Matters**
Credit data is the core of what we do, and the opportunity to make it smarter — better offer targeting, better personalization, better insight for 1,600+ financial institutions and the consumers they serve — runs directly through the ML platform. Today that platform is early. You will be the person who defines what it becomes.
Your decisions on architecture, tooling, and evaluation will set the ceiling on what every ML-driven feature at SavvyMoney can do for years.
Join us at SavvyMoney and help build the AI foundation of a platform trusted by 1,600+ banks and credit unions.
**Additionally we provide**
- Equity Compensation Package
- Flexible Time Off (FTO) - take time off as needed to rest and recharge.
- Medical, Dental, Vision – 100% premium paid for employee
- Disability/Life Insurance
- Opportunity for learning and career growth with a top Bay Area technology company
- Reimbursement for remote work setup
- Monthly stipend for phone and internet
- Team building events, culture activities, all hands events
- Paid time off to volunteer and serve the community
- Half day Fridays
- 401k matching contribution
- Beautiful California East Bay offices in Dublin, CA
**SavvyMoney’s EEO Statement**
SavvyMoney relies on diversity of culture and thought to deliver on our goal of Creative People, Practical solutions serving our client needs, and ensures nondiscrimination in all programs and activities. We continuously seek talented, qualified employees in our operations regardless of race, color, sex/gender, including gender identity and expression, sexual orientation, pregnancy, national origin, religion, disability, age, marital status, citizen status, protected veteran status, or any other protected classification under country or local law. SavvyMoney is proud to be an Equal Employment Opportunity/ Affirmative Action Employer.
We are committed to protecting your data. To learn more, please review the [**SavvyMoney Employee Privacy Policy Notice here**](https://www.savvymoney.com/employee-privacy-policy/)