2+ years of experience in software engineering focusing on production systems
Bachelor's and Master's degree in Computer Science or related field with ML/AI focus
Expertise in designing and implementing cloud-native, distributed systems
Strong Python skills plus experience with at least one lower-level language
Hands-on experience with big data technologies like Spark, Kafka, or Flink
Solid understanding of machine learning concepts, even without primary ownership
Proven ability to work cross-functionally and own project end-to-end
Responsibilities
Architect and maintain high-throughput data pipelines to process real-time signals
Design and optimize distributed systems for high transaction throughput and low latency
Build AI applications and agentic flows leveraging state-of-the-art LLMs
Own and extend internal AI agent workflows for rule creation and alert analysis
Develop pipelines for large-scale label investigation and classifier discovery
Collaborate with Data Scientists to integrate ML models into production
Implement security measures for privacy compliance in shared intelligence
Benefits
Comprehensive medical, dental, and vision coverage
Discretionary Time Off Policy (DTO) and paid holidays
Opportunities for R&D exploration and professional development
Regular team-building events and a collaborative culture
Full Job Description
Role Summary
We are hiring a AI Engineer to serve as a technical architect for our Intelligence Layer and Data Consortium. This is a specialized engineering role-distinct from general web development-focused on building the high-scale "muscle" that powers our fraud intelligence.
You will design, build, and operate distributed, production-grade services and data pipelines that ingest real-time signals from millions of users and enable our Agentic Flow to auto-tune strategies. You will own and evolve the internal AI agent workflow and tooling originally prototyped by our detection team, and help migrate it onto our new, production-grade agent framework. You will also play a key role in building AI applications and agentic flows using state-of-the-art, out-of-the-box large language models (LLMs), while partnering with Data Science and Solutions to integrate traditional machine learning models, rule engines, and label pipelines into production.
This role is first and foremost a production software engineering role. Classic ML modeling experience is a plus, but not required, as long as you bring strong software engineering fundamentals and a solid understanding of ML concepts.
Primary Responsibilities
Consortium Data Engineering Architect and maintain high-throughput data pipelines (using technologies such as Spark, Kafka, or Flink) to ingest, process, and aggregate real-time signals-such as device fingerprints and behavioral biometrics-into our central intelligence graph.
High-Scale System Design Design and optimize distributed systems to support our global data network, ensuring the platform can handle 10,000+ Transactions Per Second (TPS) with P99 latency under 150ms.
Agentic Flow & AI Application Development Build agentic flows and AI applications by leveraging state-of-the-art, out-of-the-box LLMs (e.g., OpenAI, Anthropic, Google) to enable natural language interaction, intelligent rule merging, and automated fraud strategy recommendations.
AI Agent Workflow Ownership Own and extend the internal AI agent tool and workflows used by the Solutions team for rule and feature creation, rule tuning, and alert analysis, ensuring reliable deployments across sandbox, preprod, and production solution tenants.
Label & Rule Tuning Automation Build map-reduce style LLM workflows and analytics pipelines (e.g., ClickHouse, Spark) for large-scale label investigation, weak classifier discovery, and FN/FP triage to accelerate solution onboarding and improve detection coverage.
Productionize ML Pipelines Collaborate with Data Scientists to deploy and maintain pipelines for both Unsupervised (UML) and Supervised (SML) models, integrating them with our APIs to enable real-time scoring and decisioning. Hands-on ownership of classic ML modeling is a plus, but not a strict requirement.
Privacy-First Architecture Implement robust security measures, including tokenization and hashing, to ensure PII privacy and compliance across our shared intelligence network.
Cross-Functional Collaboration Work closely with Data Science, Product, Strategy, Delivery, and Engineering teams to develop, validate, and optimize machine learning-driven features and AI-powered workflows.
Requirements
Experience 2+ years of professional software engineering experience building and shipping production systems (backend services, data platforms, or ML/AI infrastructure), ideally for customer-facing SaaS products or internal platform tools.
Education Bachelor's and Master's degree in Computer Science (or a closely related field) with a focus in Machine Learning or Artificial Intelligence.
System Architecture Proven ability to design and implement distributed, cloud-native systems for high-throughput, low-latency applications. Experience with AWS and containerization (Docker/Kubernetes) is required.
Coding Proficiency Strong, production-grade skills in Python (primary language for services and tooling), plus experience with at least one additional lower-level programming language such as Java (Go or C++ also a plus).
Big Data Technologies Hands-on experience with distributed data frameworks such as Spark, Kafka, or Flink.
Machine Learning Foundations Solid breadth and depth in ML concepts (e.g., supervised vs. unsupervised learning, feature engineering, embeddings, evaluation metrics like Precision/Recall and AUC), even if you have not been the primary model owner on a team.
Collaboration & Ownership Demonstrated ability to work cross-functionally, take end-to-end ownership of services, and operate in a fast-paced, high-impact environment.
Preferred Qualifications
Experience building or integrating LLM-powered agent workflows (e.g., LangChain/LangGraph, multi-agent orchestration, tool calling, or RAG architectures) for production or internal platforms.
Experience deploying or maintaining machine learning models (supervised or unsupervised) in production environments.
Experience working with analytical databases and large-scale data exploration (e.g., ClickHouse or other columnar data stores) is a plus.
Background in real-time decision engines or stateful stream processing.
Domain knowledge in fraud or risk (fraud detection, credit risk, payments, or trust & safety) is a strong plus but not required.
Benefits
Base Salary: 130K - 250K
Total Compensation: Includes Base + Performance Bonus + Equity Options.
Comprehensive medical, dental, and vision coverage.
Discretionary Time Off Policy (DTO) and paid holidays.
Opportunities for R&D exploration and professional development.
Regular team-building events and a collaborative, innovative culture.
About DataVisor
DataVisor is an artificial intelligence company that provides fraud detection and prevention solutions for financial services, e-commerce, and social platforms. The company was founded in 2013 and is headquartered in Santa Clara, California. DataVisor uses machine learning algorithms to analyze large amounts of data and detect fraudulent activities in real-time. The company's solutions can help businesses reduce fraud losses, improve customer experience, and increase operational efficiency. DataVisor has partnerships with major financial institutions and e-commerce companies and is backed by several venture capital firms.
