Senior Manager, Software Engineering

Experian

. You will lead engineering teams building platform capabilities and solutions for our clients using Experian bureau data and other 3rd party data .

Posted 5/20/2026full-timeRemote • 🇺🇸 United StatesSenior💰 $176,036 - $316,865 per yearWebsite

Tech Stack

Tools & technologies

AWSAzureCloudCypressDistributed SystemsDockerGoGoogle Cloud PlatformGrafanaJavaJavaScriptJMeterJUnitKafkaKubernetesNode.jsPrometheusPython

About the role

Key responsibilities & impact

You will lead engineering teams building platform capabilities and solutions for our clients using Experian bureau data and other 3rd party data
You will build data pipelines at scale for our batch clients and integrating with 3rd party providers for our real-time clients
You will also advocate for using AI and GenAI technologies across the team, including LLM-powered services, agentic workflows, MCP-based integrations, and Claude Skills for extending agent capabilities
Oversee the architecture, design, and implementation of real-time APIs with a focus on scalability, reliability, and latency
Create platform evolution, including modernization, observability, AI-enablement, and CI/CD best practices
Collaborate with partners to define the long-term vision and roadmap for the API platform, including how you will embed AI capabilities, MCP integrations, and Claude skills into the ecosystem
Ensure the team follows software engineering best practices including testing, code reviews, and documentation
Guide the use of latest technologies that support real-time processing, event streaming, performance optimization, and AI/LLM integration
Lead the delivery of AI-powered features including LLM integrations, retrieval-augmented generation (RAG), agentic workflows, MCP server/client implementations, and authoring of Claude Skills
Establish best practices for AI-enabled systems including prompt engineering, evaluation frameworks, model observability, and responsible AI guardrails
Guide the design and adoption of test harness frameworks across unit, integration, contract, performance, and end-to-end testing layers
Establish AI/LLM-specific evaluation harnesses for prompt regression, output quality scoring, hallucination detection, and evaluation of agentic and MCP-based workflows
Champion shift-left testing, automated regression suites, and quality gates integrated into CI/CD pipelines
Work with Product Management, DevOps, Data Science, QA, and other engineering teams to align technical plans with our goals
Partner with security, compliance, and infrastructure teams to ensure platform meets standards
Manage the delivery lifecycle of major platform plans
Track important performance metrics and ensure continuous improvement
Manage the delivery of insightful dashboards and data visualizations

Requirements

What you’ll need

Bachelor's degree in Computer Science, Engineering, or related field
8+ years of software engineering experience
3+ years in engineering leadership roles
Experience managing real-time, high-throughput API platforms in production environments
You have hands-on experience delivering AI/ML or GenAI projects in production
Familiarity with Model Context Protocol (MCP) and the broader AI tooling ecosystem (e.g., Claude, OpenAI, LangChain, LlamaIndex, or similar agent frameworks)
Experience authoring or working with Claude Skills (or analogous capability-extension frameworks) to package domain expertise for AI agents
Background in test engineering and quality automation, including building or scaling test harness frameworks (e.g., JUnit, pytest, TestNG, Cypress, Playwright, k6, JMeter, and Pact for contract testing)
Experience designing evaluation harnesses for AI/LLM systems
Knowledge of distributed systems, cloud platforms (AWS/GCP/Azure), and modern backend stacks (e.g., Node.js, Java, Go, or Python)
Experience with API gateways, load balancing, caching, and observability tools (Kibana, Grafana, Datadog, and Prometheus)
Familiarity with event-driven architectures, message queues (Kafka) and stream processing frameworks
Experience developing ML Ops capabilities
Data Science & ML experience
DevOps Knowledge: Docker and Kubernetes

Benefits

Comp & perks

Flexible Time Off: 20 Days
Great compensation package and bonus plan
Core benefits including medical, dental, vision, and matching 401K
Flexible work environment, ability to work remote, hybrid or in-office
Flexible time off including volunteer time off, vacation, sick and 12-paid holidays

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

software engineeringAI/MLGenAIreal-time APIstest engineeringquality automationevaluation harnessesML Opsdistributed systemscloud platforms

Soft Skills

leadershipcollaborationadvocacycommunicationproject managementcontinuous improvementproblem-solvingstrategic planningteam managementstakeholder engagement

Certifications

Bachelor's degree in Computer ScienceBachelor's degree in Engineering