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About This Role
As a Senior AI Site Reliability Engineer, you will play a pivotal role in building and operating the next\-generation, AI\-first Electronic Health Record platform. In this role, you will design, build, and operate highly reliable, scalable infrastructure and data pipelines that power mission\-critical analytics globally.
You will also contribute to the next evolution of cloud operations by advancing automation, observability, and AI\-assisted reliability practices. This includes exploring the use of Generative AI and intelligent automation to improve incident response, system resilience, and operational efficiency.
You will work within a collaborative team to deliver robust solutions that handle massive datasets with precision and performance, while continuously improving system reliability and operational excellence.
*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
Infrastructure \& Reliability
- Experience building and operating high\-availability, fault\-tolerant systems
- Strong understanding of distributed systems, performance monitoring, and resiliency patterns
- Experience with incident response, root\-cause analysis, and production troubleshooting
AI\-Native Engineering (NEW)
- Hands\-on experience applying Generative AI or Agentic AI (e.g., LangChain, AutoGPT, custom agents) to:
+ Infrastructure lifecycle management
+ Observability and anomaly detection
+ Incident response and remediation automation
- Ability to design or integrate AI\-driven workflows for operational efficiency and reliability
- Familiarity with building or integrating autonomous agents for DevOps/SRE use cases
Cloud \& Multi\-Cloud Ecosystems
- Strong experience with multi\-cloud environments (OCI, AWS/Azure)
- Deep understanding of cloud infrastructure design, deployment, and resource optimization
- Experience managing hybrid or cross\-cloud architectures
DevOps/SRE Practices
- Advanced competency in CI/CD pipelines (Jenkins, Kubernetes)
- Infrastructure as Code (Terraform)
- Observability tools (Prometheus, Grafana)
- Strong focus on automation\-first operations
Data Technologies
- Proficiency in Data Warehousing platforms (e.g., Vertica, Snowflake)
- Experience with ETL frameworks and large\-scale data processing
- Understanding of columnar storage systems
BI \& Reporting
- Experience supporting or integrating BI tools (Tableau, Power BI, Oracle Analytics)
Programming \& Tools
- Strong proficiency in Python, Java, or Go
- Experience with Docker, Kubernetes, and shell scripting
Problem\-Solving
- Strong troubleshooting skills with ability to perform root\-cause analysis
- Experience resolving complex production issues in distributed systems
Develop \& Maintain
- Implement and optimize infrastructure for Oracle HDI Analytics Platform
- Ensure system uptime, reliability, and scalability
AI\-Driven Automation (NEW)
- Design and implement GenAI\-powered or agent\-based solutions for:
+ Observability and anomaly detection
+ Incident triage and remediation
+ Infrastructure provisioning and lifecycle management
- Build tools and frameworks that enable self\-service and autonomous operations
Data Pipeline Execution
- Build and optimize scalable data pipelines using Vertica and ETL frameworks
Operational Excellence
- Apply DevOps/SRE practices to automate deployments and operations
- Enhance observability using Prometheus/Grafana and AI\-driven insights
Cloud Integration
- Support multi\-cloud initiatives across OCI, AWS, and Azure
- Optimize cost, performance, and compliance across environments
Incident Response
- Participate in on\-call rotations
- Implement preventative and automated remediation solutions
Collaboration
- Work closely with engineers to execute technical roadmaps
- Contribute to code reviews and infrastructure improvements
What You Bring
- 4\+ years of software engineering, cloud infrastructure, SRE, or DevOps experience
- Proven ownership of production system reliability in cloud environments
Core Expertise
- Cloud infrastructure design and automation
- Distributed systems and performance optimization
- Data warehousing and ETL frameworks
AI\-Native Experience
- Demonstrated experience applying GenAI / LLMs / agentic frameworks to infrastructure or operations
- Experience building or integrating AI\-powered automation for DevOps/SRE workflows
- Familiarity with tools like LangChain, AutoGPT, or custom AI agents
Technical Skills
- Terraform, Docker, Kubernetes
- Observability stacks (Prometheus, Grafana)
- Python, Java, or Go
Additional Strengths
- Strong problem\-solving mindset with a focus on automation and scalability
- Experience improving system reliability through intelligent automation
Preferred Qualifications
- Experience in healthcare or regulated environments (HIPAA, compliance frameworks)
- Experience working in environments requiring security clearance
- Experience building self\-healing or autonomous infrastructure systems
Responsibilities
Work with the Site Reliability Engineering (SRE) team to take shared ownership of services and platform components. Develop a strong understanding of end\-to\-end system architecture, dependencies, and production behavior.
- Design, build, and operate reliable, scalable, and secure infrastructure supporting large\-scale analytics workloads
- Improve system reliability through automation, monitoring, and performance optimization
- Contribute to the adoption of AI\-assisted approaches for operations, including:
Enhancing observability and alerting
Supporting automated incident detection and remediation
Exploring intelligent automation for infrastructure lifecycle management
- Partner with development teams to enhance service architecture, scalability, and operability
- Participate in on\-call rotations and act as an escalation point for complex production issues
- Perform root cause analysis and implement long\-term fixes to prevent recurrence
- Apply knowledge of distributed systems to troubleshoot issues and optimize system performance
- Drive continuous improvement in DevOps/SRE practices, including CI/CD, Infrastructure as Code, and automation at scale
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Oracle, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $181,170 based on 12,692 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 $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Oracle AI Hiring
Oracle has 22 open AI roles right now. They're hiring across AI/ML Engineer, AI Agent Developer, AI Software Engineer, MLOps Engineer. Positions span US, Seattle, WA, US.
Location Context
AI roles in Austin pay a median of $215,300 across 523 tracked positions. That's 8% above the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, 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 $275,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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