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About This Role
Position: Technical Advisor
Employment Type: Part\-Time / Hourly
Work Location: Remote
Engagement: Hourly / Consulting
Customer Focus: U.S. Federal Government
Position Overview
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We are seeking an experienced Technical Advisor to provide part\-time, senior\-level technical guidance and solutioning support for our internal teams and Federal Government customer engagements.
The Technical Advisor will serve as a trusted technical resource responsible for developing and reviewing technical solutions, architectures, approaches, and responses across Cloud, Application, Data, Artificial Intelligence, Cybersecurity, and other emerging technology areas.
This is an advisory and solutioning\-focused role. The ideal candidate should be able to quickly understand customer requirements, translate business and mission needs into practical technical solutions, and help teams develop compelling and technically sound approaches for Federal customers.
Key Responsibilities
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- Serve as an internal technical advisor and subject matter expert for Federal customer opportunities and projects.
- Develop high\-level and detailed technical solutions and solution architectures based on customer requirements.
- Provide technical expertise across:
+ Cloud: Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform (GCP)
+ Application Architecture \& Modernization
+ Data Engineering, Data Platforms \& Analytics
+ Artificial Intelligence (AI), Generative AI (GenAI) \& Machine Learning
+ Application Programming Interfaces (APIs) and Microservices
+ Cybersecurity and Zero Trust
+ DevSecOps, Infrastructure as Code (IaC) and Cloud Automation
+ Containers, Kubernetes and Cloud\-Native Architecture
+ Data \& AI platforms, Large Language Models (LLMs), Retrieval\-Augmented Generation (RAG) and AI\-enabled solutions
+ Other emerging and high\-growth technologies relevant to Federal IT modernization.
- Analyze Requests for Information (RFIs), Requests for Proposals (RFPs), Statements of Work (SOWs), Performance Work Statements (PWSs), and Statements of Objectives (SOOs) and translate requirements into technical approaches.
- Develop technical solution concepts, architecture diagrams, technology stacks, implementation approaches, and solution narratives.
- Support proposal solutioning, technical writing, and technical reviews.
- Collaborate with business development, capture, proposal, recruiting, and delivery teams to develop technically competitive solutions.
- Evaluate emerging technologies and recommend where they can provide value to Federal customers.
- Review proposed technical approaches for feasibility, scalability, security, cost, and alignment with Federal requirements.
- Provide technical mentorship and guidance to internal teams.
- Participate in customer discussions, technical briefings, solution presentations, and architecture reviews when required.
- Help identify technology partners, platforms, tools, and technical capabilities needed to support customer requirements.
Required Qualifications
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- 10\+ years of progressive experience in technology, IT architecture, engineering, consulting, or technical solutioning.
- Demonstrated experience developing technical solutions and architectures for complex enterprise environments.
- Strong knowledge of at least two major cloud platforms, preferably AWS, Azure, and/or GCP.
- Broad understanding of modern Application, Data, Cloud, AI, and Cybersecurity technologies.
- Experience translating complex technical requirements into clear, actionable solution approaches.
- Strong technical writing and presentation skills.
- Experience supporting Federal Government customers, contracts, proposals, or solutioning efforts.
- Ability to work independently in a part\-time advisory capacity and provide expertise when needed.
- Ability to communicate complex technical concepts effectively to both technical and non\-technical stakeholders.
Preferred Qualifications
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- Experience with Federal IT modernization and cloud transformation.
- Experience with Enterprise Architecture, Cloud Architecture, Solution Architecture, or Technical Architecture.
- Knowledge of Federal technology and security frameworks such as:
+ National Institute of Standards and Technology (NIST)
+ Federal Risk and Authorization Management Program (FedRAMP)
+ Federal Information Security Modernization Act (FISMA)
+ Zero Trust Architecture
+ NIST Cybersecurity Framework
- Experience with Artificial Intelligence, Generative AI, Machine Learning, Large Language Models, Retrieval\-Augmented Generation, AI Agents, or AI governance.
- Experience with cloud\-native technologies, Kubernetes, containers, Infrastructure as Code, and DevSecOps.
- Experience supporting Requests for Proposals (RFPs), Requests for Information (RFIs), Sources Sought, and government technical responses.
- Relevant certifications such as:
+ AWS Certified Solutions Architect
+ Microsoft Certified: Azure Solutions Architect Expert
+ Google Cloud Professional Cloud Architect
+ Certified Information Systems Security Professional (CISSP)
+ Certified Cloud Security Professional (CCSP)
+ TOGAF certification
+ Other relevant cloud, architecture, cybersecurity, data, or AI certifications.
Ideal Candidate
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The ideal candidate is a technology generalist with deep expertise in architecture and solutioning rather than someone limited to a single technology stack.
You should be able to walk into a Federal customer requirement, understand the mission and technical challenges, and answer:
“What should we build, how should we build it, what technologies should we use, and why is this the right solution?”
The candidate should be comfortable moving between Cloud \+ Application \+ Data \+ AI \+ Security \+ Emerging Technologies and providing practical, commercially viable recommendations.
Engagement Details
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- Part\-Time
- Remote
- Hourly Consulting Engagement
- Flexible hours based on project and proposal requirements
- Primarily internal advisory and Federal customer support
- Opportunity to support multiple Federal technology initiatives and proposals
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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
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 $180,000 across 1,196 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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