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About This Role
ERP Suites develops Enterprise AI agents and orchestration solutions hosted on Oracle Cloud Infrastructure (OCI). Our products automate complex finance, supply chain, and operational workflows for enterprise customers.
We are seeking an AI Platform Engineer who thrives in a high\-ownership environment and is passionate about building, operating, and scaling the infrastructure that powers next\-generation AI solutions.
Location:
Our home office is based in Cincinnati, OH. However, we are open to hiring someone who is fully remote regardless of location. Although the position will be remote, there might be some occasional travel to ERP Suites facilities or customer sites.
Position Summary:
The AI Platform Engineer will be responsible for the platform foundation that supports ERP Suites' AI products and customer environments. This role partners closely with the AI Architect and Product Team to deploy, manage, secure, and optimize Oracle Cloud Infrastructure environments, automation pipelines, and AI agent deployment frameworks.
The ideal candidate is a hands\-on cloud and infrastructure professional with experience in DevOps, AI platform operations, automation, security, observability, and enterprise cloud architecture.
Daily Activities:
- Monitor OCI service health, logs, dashboards, and alerts.
- Troubleshoot platform issues and customer environment concerns.
- Support development teams with infrastructure questions and deployment needs.
- Manage CI/CD pipeline performance and resolve deployment failures.
- Execute provisioning, onboarding, and configuration requests.
- Update documentation and architecture artifacts as infrastructure evolves.
- Participate in standups, planning meetings, and technical reviews.
Monthly Activities:
- Review OCI consumption reports, billing dashboards, and cost optimization opportunities.
- Conduct IAM, security, and credential audits.
- Evaluate reference architecture environments for configuration drift and required updates.
- Refine deployment methodologies, runbooks, and onboarding documentation.
- Assess Oracle OCI roadmap updates and emerging platform capabilities.
- Contribute technical documentation, architecture guidance, and internal knowledge\-sharing content.
Key Responsibilities:
Cloud Infrastructure \& DevOps:
- Provision, configure, and manage Oracle Cloud Infrastructure (OCI) environments, including computer, networking, load balancers, API gateways, IAM, containers, and related services.
- Manage OCI Functions, Autonomous Database Serverless (ADB\-S), and containerized deployment environments.
- Build, maintain, and optimize OCI DevOps pipelines, artifact repositories, and deployment automation.
- Support OCI Goldengate planning, configuration, and data replication architectures.
- Develop automation solutions that improve reliability, scalability, and operational efficiency.
AI Agent Deployment \& Operations:
- Own customer\-facing AI agent deployment methodologies, runbooks, environment configurations, and deployment standards.
- Coordinate customer environment provisioning, compartment creation, IAM setup, and onboarding activities.
- Manage AI agent environments across development, testing, and production stages.
- Support development teams through infrastructure reviews, deployment guidance, and technical troubleshooting.
- Maintain and extend ERP Suites' enterprise reference architectures and deployment frameworks.
Monitoring, Observability \& FinOps:
- Build and maintain Grafana dashboards and reporting solutions for operational monitoring and customer billing.
- Develop ETL processes that aggregate OCI cost and consumption data.
- Monitor platform health, performance, reliability, and resource utilization.
- Diagnose and resolve observability gaps before they impact customer environments.
- Ensure accurate reporting and billing visibility across customer environments.
Security \& Governance:
- Audit OCI IAM policies, Vault usage, credential management processes, and security controls.
- Maintain TLS certificate automation using ACME, Let's Encrypt, and OCI Load Balancer integrations.
- Support secure architecture reviews and infrastructure compliance initiatives.
- Ensure proper access controls, credential rotation, and security best practices across environments.
Technical Architecture \& Documentation:
- Create and maintain architecture diagrams, infrastructure maps, deployment workflows, and technical documentation.
- Document automation scripts, deployment processes, and operational procedures.
- Participate in technical planning sessions with customers and internal stakeholders.
- Identify infrastructure risks and recommend scalable solutions.
Qualifications:
Required:
- Bachelor’s degree in computer science, Information Systems, Engineering, or a related field.
- 2\+ years of experience in AI Platform Engineering, Infrastructure Engineering, MLOps, DevOps, or Cloud Engineering.
- Strong experience with Oracle Cloud Infrastructure (OCI), including:
- Experience deploying and supporting AI agents, microservices, or cloud\-native applications.
- Experience with monitoring and observability platforms such as Grafana, LangFuse, OCI Logging, and Metrics APIs.
- Knowledge of TLS, DNS, ACME protocols, Let's Encrypt, and certificate automation.
- Experience with CI/CD tools, source control, deployment pipelines, and artifact management.
- Proficiency in Python, SQL, and Bash scripting.
- Strong technical writing, documentation, and architecture diagramming skills.
- Excellent communication and collaboration skills.
Core Competencies:
- Cloud infrastructure architecture and administration
- AI platform operations and deployment
- DevOps and CI/CD automation
- Monitoring, observability, and FinOps
- Security architecture and identity management
- Infrastructure\-as\-Code and automation
- Technical troubleshooting and root cause analysis
- Customer\-facing technical consulting
- Documentation and knowledge transfer
Preferred Qualifications:
- Oracle Cloud certifications such as OCI Architect Professional or OCI DevOps Professional.
- Experience supporting multi\-tenant SaaS or managed\-service environments.
- Exposure to large language model (LLM) infrastructure and agentic AI frameworks such as LangChain, MCP, or similar technologies.
- Experience implementing AI observability platforms such as LangFuse, MLflow, or equivalent tools.
- Familiarity with JD Edwards EnterpriseOne, including CNC, AIS, Orchestrator Studio, or security administration.
- Experience participating in Oracle Partner Network, Oracle ACE, or similar technical communities.
Company:
At ERP Suites, our focus is on helping our customers realize IT’s potential. Our comprehensive ERP solutions enable them to streamline and scale their IT products and processes. And this leads directly to improved efficiency and increased margins. We are a proud Oracle Gold Partner and champion of proactive JD Edwards management and custom product enhancements.
ERP Suites provides technical consulting, cloud services, managed services, and digital transformation solutions for some of America’s top companies. We build secure connections, improve performance, automate workloads and give them mobility. In other words, we help them stay on top.
We deliver multi\-functional value through cloud services, digital transformation, ERP consulting services, ERP managed services, and software development.
Core Values:* Make Customers Successful
- Be An Advisor
- Be a teacher
- Be a Coach
- Have Fun
- Do the Right Things for the ERP Suites Family
- Adapt Quickly to Changing Roles and Environments
This is Where IT Change Starts.* With questions.
- With problems that need to be solved.
- With business needs, both immediate and long term.
- Because technology and its impact on business isn’t getting any simpler.
- That’s why we exist.
- To answer the tough questions.
- To find a solution to every problem—no matter the size or scope.
- And to help companies not just identify IT’s potential, but realize IT
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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 ERP Suites, 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. Mid-level AI roles across all categories have a median of $165,000.
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.
ERP Suites AI Hiring
ERP Suites has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,926 positions. About 15% of all AI roles offer remote work.
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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