Responsibilities
- Lead the architecture and development of cloud-native applications, services, and platforms using Python and AWS.
- Build and manage cloud-native microservices leveraging AWS services such as Lambda, ECS, S3, Glue, RDS/DynamoDB, EMR, Glue and EKS.
- Provide technical leadership across engineering teams, driving design reviews, architecture decisions, coding standards, and operational excellence.
- Partner with product, security, architecture, data science, and platform teams to translate business problems into scalable technical solutions.
- Develop reusable frameworks, libraries, APIs, and automation patterns that accelerate engineering delivery.
- Drive adoption of engineering best practices including CI/CD, infrastructure as code, test automation, observability, secure SDLC.
- Build and optimize large‑scale distributed data processing solutions using Apache Spark, PySpark, SQL, and cloud data services.
- Evaluate emerging AI engineering tools, LLM frameworks, model providers, and agentic development platforms, recommending practical adoption paths.
- Design, build, and maintain secure, lightweight Docker images for Python, Spark, and GenAI applications.
- Build and maintain Infrastructure as Code using Terraform for AWS cloud resources and platform components.
- Reporting to a Senior Manager.
Qualifications
- 11+ years of software engineering experience, with significant experience designing and delivering enterprise‑scale systems.
- Deep experience with AWS, including services such as Lambda, ECS/EKS, API Gateway, S3, IAM, EMR, CloudWatch, Step Functions, EventBridge, RDS/DynamoDB, Bedrock, and Glue or equivalent cloud‑native services.
- Build application in React, TypeScript, and modern JavaScript (ES6+).
- Practical experience with Generative AI concepts and implementation patterns, including LLM integration, prompt engineering, embeddings, vector databases, RAG, AI safety, and model evaluation.
- Strong hands‑on expertise in Python, including API development, backend services, automation, testing, packaging, and production‑grade code quality.
- Strong working knowledge of Jupyter notebooks for experimentation, prototyping, and analytical development.
Technical Skills
- Languages: Python, SQL, PySpark, TypeScript.
- Cloud: AWS, S3, Lambda, Glue, EMR, SageMaker, Bedrock, IAM, CloudWatch.
- AI/GenAI: LLMs, RAG, embeddings, AI agents, model evaluation.
- Data: Spark, distributed processing, data lakes, ETL/ELT, batch.
- Tools: Jupyter, Git, CI/CD, Docker, Kubernetes, Terraform or CloudFormation.
- Engineering: APIs, microservices, observability, testing, security, architecture design.
Benefits
Experian cares for employee's work‑life balance, health, safety and wellbeing. In support of this endeavor, we offer best‑in‑class family well‑being benefits, enhanced medical benefits and paid time off.
Equal Opportunity Employer
Experian is proud to be an Equal Opportunity and affirmative‑action employer. Innovation is a critical part of Experian's DNA and practices, and our diverse workforce drives our success. Everyone can succeed at Experian and bring their whole self to work, irrespective of their gender, ethnicity, religion, color, sexuality, physical ability or age. If you have a disability or special need that requires accommodation, please let us know at the earliest opportunity.