Principal Software Engineer - AI and Insights Platform

NCR Corporation•Atlanta, GA

About The Position

We are seeking a highly experienced Principal Software Developer to lead the design, development, and delivery of next-generation AI-powered applications and enterprise data platforms. This role requires deep expertise in Google Cloud Platform (GCP), Databricks, Generative AI/AI Agents, and modern Full Stack Development. As a Principal Developer, you will provide technical leadership across architecture, engineering standards, cloud modernization, AI adoption, data platform integration, and application development. You will collaborate with product, data engineering, analytics, and business stakeholders to build scalable, secure, and high-performance solutions.

Requirements

Bachelor's or Master's degree in Computer Science, Engineering, or related field.
10+ years of software development experience with enterprise applications.
5+ years of cloud-native application development experience.
3+ years of hands-on experience with Databricks and Spark ecosystem.
3+ years of experience building AI/ML or Generative AI solutions.
Strong expertise in GCP architecture and services.
Strong full-stack development experience using modern web technologies.
Cloud: GCP, Kubernetes, Docker, Cloud Run, GKE, BigQuery, Vertex AI
Data Platforms: Databricks, Delta Lake, Apache Spark, SQL, Data Lakehouse Architecture
AI/ML: Generative AI, LLMs, RAG, Vector Databases, LangChain, LangGraph, AI Agents
Backend: Python, Java, Node.js, REST APIs, Microservices
Frontend: React, Next.js, TypeScript, JavaScript, HTML5, CSS
DevOps: GitHub Actions, Jenkins, Terraform, CI/CD, Infrastructure as Code
Databases: SQL Server, PostgreSQL, BigQuery, NoSQL Platforms

Nice To Haves

Experience developing AI-powered enterprise applications.
Experience with retail, payments, commerce, or enterprise analytics platforms.
Knowledge of MLOps, Model Monitoring, AI Governance, and Observability.
GCP Professional Cloud Architect or Professional Machine Learning Engineer certification.
Databricks Certified Data Engineer or Architect certification.
Experience leading globally distributed engineering teams.
Experience with large-scale data modernization and cloud migration programs.
Strategic thinker with strong architectural vision.
Excellent communication and stakeholder management skills.
Ability to influence technical decisions across multiple teams.
Strong mentoring and team development capabilities.
Results-driven with a focus on innovation, quality, and operational excellence.

Responsibilities

Design and build enterprise-grade AI and Generative AI solutions leveraging LLMs, AI Agents, RAG architectures, vector databases, and prompt engineering.
Develop intelligent applications using Google AI services, Vertex AI, Gemini, LangChain, LangGraph, and related AI frameworks.
Drive AI platform architecture, governance, observability, and responsible AI practices.
Implement scalable AI inference and model deployment pipelines.
Lead development of cloud-native web applications and APIs.
Design and implement modern front-end solutions using React, Next.js, TypeScript, JavaScript, HTML5, and CSS.
Build scalable backend services using Java, Python, Node.js, or similar technologies.
Architect microservices and event-driven systems supporting enterprise-scale workloads.
Drive best practices in software craftsmanship, code quality, automated testing, and CI/CD.
Design and develop modern data architectures on Databricks Lakehouse platform.
Build scalable ETL/ELT pipelines using Databricks, Spark, Delta Lake, and SQL.
Optimize data processing performance, governance, lineage, and cost management.
Integrate AI workloads with Databricks Data Intelligence Platform.
Lead cloud architecture and implementation across GCP services.
Develop solutions using: Vertex AI, BigQuery, Cloud Run, Cloud Functions, GKE (Google Kubernetes Engine), Pub/Sub, Cloud Storage, Cloud SQL, IAM & Security Services.
Establish cloud architecture standards, reliability, scalability, and security frameworks.
Serve as a principal technical advisor and mentor to engineering teams.
Define architecture standards, development practices, and technology roadmaps.
Review solution designs and provide guidance on scalability, resiliency, and maintainability.
Partner with Product Management, Data Engineering, Analytics, and Architecture teams to deliver business outcomes.
Lead technical evaluations, POCs, and adoption of emerging AI and cloud technologies.