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About This Role
Join Oracle's Health Data Intelligence (HDI) team as a Principal Software Engineer, where you will design and build the next generation of cloud\-native platforms, distributed systems, and intelligent automation solutions that power large\-scale healthcare analytics.
This role is ideal for engineers who enjoy solving complex software engineering challenges at scale. You will develop highly available services, reliability platforms, observability systems, automation frameworks, and AI\-powered operational tooling that enable mission\-critical analytics workloads across Oracle Cloud Infrastructure and multi\-cloud environments.
You will partner with product, platform, data, and reliability teams to build scalable software systems that process massive datasets, improve developer productivity, automate operational workflows, and enhance platform resilience.
As Oracle continues investing in AI\-native infrastructure, you will help drive the adoption of Generative AI and agent\-based technologies to build intelligent operational platforms, self\-service infrastructure solutions, and autonomous reliability capabilities.
U.S. citizenship is required for this position, as the successful candidate will be required to obtain and maintain a U.S. government security clearance after hire.
Required Skills
Software Engineering
- Strong software development experience in Python, Java, Go (Golang), or similar languages
- Strong hands\-on system design experience with the ability to architect and build large\-scale distributed systems
- Demonstrated expertise writing high\-quality, maintainable, testable, and production\-grade code
- Strong understanding of software architecture, design patterns, and engineering best practices
- Experience developing cloud\-native applications, microservices, and platform services
- Experience leading technical design discussions, architecture reviews, and complex engineering initiatives
Distributed Systems \& Platform Engineering
- Experience building highly available, fault\-tolerant distributed systems at scale
- Strong understanding of scalability, concurrency, resiliency, performance optimization, and reliability patterns
- Experience developing platform services, shared frameworks, developer tooling, and self\-service platforms
- Knowledge of event\-driven architectures, service\-oriented systems, and asynchronous processing patterns
AI\-Native Engineering
- Hands\-on experience building solutions using Generative AI, Agentic AI, Large Language Models (LLMs), and intelligent automation technologies
- Experience integrating frameworks such as LangChain, AutoGen, CrewAI, Semantic Kernel, OpenAI, or equivalent AI platforms
- Experience building AI\-powered automation for:
+ Incident investigation and root cause analysis
+ Operational intelligence and observability
+ Infrastructure lifecycle management
+ Engineering productivity and developer experience
- Experience designing APIs, services, and platforms that incorporate AI capabilities
- Experience building AI\-assisted operational tooling, autonomous remediation systems, or intelligent platform services is highly desirable
Cloud \& Infrastructure Engineering
- Strong experience with OCI, AWS, Azure, or multi\-cloud environments
- Experience building cloud\-native services using Kubernetes, Docker, and container orchestration platforms
- Strong understanding of cloud architecture, networking, security, compliance, and cost optimization
- Deep experience with Infrastructure as Code (IaC) using Terraform, Ansible, and related automation frameworks
- Experience building infrastructure automation, deployment tooling, and platform engineering solutions
Data Engineering
- Experience building data\-intensive applications and analytics platforms
- Knowledge of ETL pipelines and large\-scale data processing frameworks
- Familiarity with data warehouse technologies such as Snowflake, Vertica, or equivalent platforms
- Understanding of distributed storage systems, columnar databases, and large\-scale analytics architectures
Reliability Engineering
- Strong understanding of SRE principles and operational excellence practices
- Experience implementing observability solutions using Prometheus, Grafana, OpenTelemetry, or similar technologies
- Experience analyzing production issues and implementing durable engineering solutions
- Knowledge of monitoring, alerting, reliability engineering, performance tuning, and self\-healing systems
What You Bring
- 10\+ years of hands\-on software engineering experience designing, building, and operating large\-scale distributed systems
- Proven experience delivering production software in cloud\-native environments
- Strong track record of leading complex technical initiatives from architecture and design through deployment and operations
- Experience building platform services, developer tooling, infrastructure automation frameworks, or large\-scale analytics platforms
Core Technical Expertise
- Large\-scale distributed systems architecture and hands\-on system design
- Software engineering with strong coding proficiency in Python, Java, and/or Go
- Cloud\-native application development and microservices architecture
- Infrastructure as Code (Terraform, Ansible) and automation engineering
- Platform engineering and developer productivity tooling
- Large\-scale data processing and analytics systems
- Performance optimization, scalability, resiliency, and reliability engineering
- AI\-powered platforms, intelligent automation, and agent\-based system development
AI\-Native Experience
- Experience building AI\-powered software products, engineering platforms, or operational tooling
- Experience integrating LLMs, agent frameworks, RAG architectures, and intelligent automation systems into production environments
- Understanding of emerging AI engineering patterns and practical applications within software engineering, infrastructure, and operations
Technical Skills
- Python, Java, Go (Golang)
- Terraform, Ansible, Infrastructure as Code (IaC)
- Kubernetes, Docker
- CI/CD and DevOps platforms
- Prometheus, Grafana, OpenTelemetry
- Cloud platforms (OCI preferred)
- Generative AI, Agentic AI, LLM frameworks, and AI\-powered automation platforms
Role Details
About This Role
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 3,824 AI roles we're tracking, AI Software Engineer positions make up 7% of the market. At Oracle, this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills Required
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $234,620 based on 682 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Oracle AI Hiring
Oracle has 12 open AI roles right now. They're hiring across AI Software Engineer, AI/ML Engineer, Research Scientist, AI Agent Developer. Positions span US, Austin, TX, US.
Location Context
AI roles in Austin pay a median of $218,800 across 493 tracked positions. That's 9% above the national median.
Career Path
Common paths into AI Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
AI Hiring Overview
The AI job market has 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 roles).
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
The AI Job Market Today
The AI job market spans 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (119) are outnumbered by mid-level (1,813) and senior (1,472) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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