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About This Role
Position Overview
=====================
We are seeking an experienced Principal AI Solutions Architect to lead the design and implementation of secure, scalable, and enterprise\-grade AI and cloud solutions supporting large\-scale digital transformation initiatives.
This role requires deep expertise in AWS cloud architecture, Generative AI ecosystems, enterprise systems integration, MLOps, AI governance, and cloud\-native application design. The ideal candidate will serve as a strategic technical leader responsible for architecting AI\-enabled platforms while ensuring security, compliance, governance, and operational excellence.
The architect will collaborate closely with enterprise leadership, data engineering teams, cybersecurity, DevSecOps, and application development teams to drive innovation using modern AI technologies including LLMs, RAG architectures, MCP (Model Context Protocol), AI Guardrails, and enterprise AI orchestration frameworks.
Key Responsibilities
========================
- Design and implement enterprise\-scale AWS cloud architectures supporting AI/ML and GenAI workloads.
- Architect scalable, resilient, and secure cloud\-native solutions using:
+ Microservices
+ Containers
+ APIs
+ Event\-driven architectures
+ Serverless computing
- Lead architecture and integration efforts for:
+ Generative AI platforms
+ LLM orchestration
+ AI agents
+ RAG pipelines
+ Vector databases
+ Knowledge graph integrations
- Design and implement AI governance frameworks including:
+ Responsible AI
+ AI Guardrails
+ Model monitoring
+ Risk management
+ Compliance controls
- Implement and support MCP (Model Context Protocol) integrations and AI interoperability solutions.
- Architect enterprise MLOps pipelines for:
+ Model training
+ Validation
+ Deployment
+ Observability
+ Lifecycle management
- Design secure AI solutions leveraging AWS services such as:
+ SageMaker
+ Bedrock
+ Lambda
+ ECS/EKS
+ API Gateway
+ DynamoDB
+ S3
+ IAM
+ CloudWatch
- Collaborate with DevSecOps teams to implement:
+ CI/CD pipelines
+ Infrastructure as Code (Terraform/CloudFormation)
+ Security automation
+ Compliance scanning
- Develop architecture standards, governance models, and technical roadmaps.
- Provide technical leadership, mentoring, and architectural guidance across engineering teams.
- Evaluate emerging AI technologies and recommend enterprise adoption strategies.
- Support enterprise modernization initiatives aligned with scalable AI adoption and cloud transformation goals.
Required Qualifications
===========================
- 10\+ years of experience in:
+ Enterprise Architecture
+ Cloud Engineering
+ Systems Architecture
+ Solution Architecture
- 5\+ years of hands\-on AWS cloud architecture experience.
- Strong expertise in:
+ AI/ML architecture
+ Generative AI
+ Large Language Models (LLMs)
+ AI orchestration frameworks
+ MLOps
- Experience with:
+ MCP (Model Context Protocol)
+ AI Guardrails
+ Responsible AI frameworks
- Strong experience designing:
+ Distributed systems
+ Enterprise integrations
+ Cloud\-native platforms
- Expertise in:
+ API architecture
+ Event\-driven systems
+ Kubernetes
+ Docker
+ Serverless computing
- Experience with Infrastructure as Code:
+ Terraform
+ CloudFormation
- Strong DevSecOps and CI/CD implementation experience.
- Strong understanding of:
+ Security architecture
+ Governance
+ Compliance
+ Enterprise cloud controls
- Strong communication and stakeholder management skills.
Preferred Qualifications
============================
- AWS Certified Solutions Architect – Professional
- AWS AI/ML Specialty Certification
- Experience with:
+ LangChain
+ OpenAI APIs
+ Bedrock Agents
+ Vector databases
+ Knowledge Graphs
+ RAG architectures
- Experience implementing enterprise AI governance and Responsible AI initiatives.
- Familiarity with enterprise modernization and digital transformation initiatives.
Technical Skills
====================
- AWS Cloud Architecture
- Generative AI / GenAI
- LLM Integration
- MCP (Model Context Protocol)
- AI Guardrails
- Responsible AI
- MLOps
- Terraform / CloudFormation
- Kubernetes / Docker
- Python
- API Design
- DevSecOps
- CI/CD Pipelines
- Enterprise Systems Integration
- Data \& AI Governance
- RAG Architectures
- Vector Databases
- Bedrock / SageMaker
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 Oran Inc, 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.
Oran Inc AI Hiring
Oran Inc has 1 open AI role 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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