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About This Role
Orion Innovation is a premier, award\-winning, global business and technology services firm. Orion delivers game\-changing business transformation and product development rooted in digital strategy, experience design, and engineering, with a unique combination of agility, scale, and maturity. We work with a wide range of clients across many industries including financial services, professional services, telecommunications and media, consumer products, automotive, industrial automation, professional sports and entertainment, life sciences, ecommerce, and education.
Overview
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The Consultant will lead a structured assessment and advisory engagement across the client's Data Protection Fleet, covering three core practice areas (Cryptography \& Secrets, Data Leakage Prevention, Data Asset Protection), the Data Protection Services Policy and Architecture group, and the horizontal functions of fleet enablement, development, and engineering. The engagement will identify and catalogue AI\-enabled opportunities across processes, workflows, service consumption, and engineering delivery, culminating in a target\-state roadmap and executive\-ready findings report.
Key Responsibilities
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Mobilization \& Current\-State Assessment
- Establish engagement plan, governance cadence, interview schedule, and workstream structure
- Confirm in\-scope verticals, stakeholder groups, and required artefacts
- Conduct stakeholder interviews and working sessions across pillars and functions
- Document current\-state operating model, processes, decision points, and friction points
Domain Coverage
- Cryptography \& Secrets: key/certificate lifecycle, secrets onboarding and rotation, vault operations, privileged secrets handling, service\-request fulfilment
- Data Leakage Prevention: DLP policy/ruleset engineering, alert triage and tuning, governance workflows
- Data Asset Protection: data scanning operations, permissions/entitlement analytics, DSAR workflow support, remediation coordination
- Policy \& Architecture: standards authoring, architecture review support, control interpretation
- Fleet Enablement, Development \& Engineering: roadmap governance, SDLC standardization, engineering standards, tooling consistency
AI Opportunity Identification \& Cataloguing
- Identify AI\-enabled use cases across processes, workflows, and engineering delivery for each in\-scope pillar
- Define opportunities for pillar\-specific agents and a cross\-pillar orchestration agent
- Populate and maintain the AI Initiatives Tracker (Innovation Backlog, AI Initiatives Register, Risk \& Controls, Benefits Tracking)
- Map current or recommended AI tooling (e.g., M365 Copilot, GitHub Copilot, Claude Code, ChatGPT) to each use case
Feasibility, Controls \& Roadmap
- Assess technical feasibility, control requirements, and data\-handling implications for prioritized use cases
- Define target\-state operating model, human\-in\-the\-loop points, and governance forums
- Prioritize opportunities and build a phased implementation roadmap (near/medium/long\-term)
Executive Reporting \& Knowledge Transfer
- Prepare executive\-ready findings report and leadership presentation
- Lead validation workshops and readout/knowledge\-transfer sessions with client stakeholders
Required Qualifications
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- Senior\-level consulting experience in data security, data protection, or cybersecurity advisory engagements, ideally within Tier\-1 financial services
- Working knowledge across two or more of: cryptography/secrets management (e.g., HashiCorp Vault, PKI), DLP platforms, data discovery/classification and scanning tools, or security architecture and policy
- Demonstrated experience identifying and scoping AI/automation opportunities within enterprise operating models
- Strong stakeholder management skills; comfortable leading interviews, workshops, and executive readouts
- Experience producing operating\-model documentation, use\-case catalogues, and roadmap artifacts
Preferred Qualifications
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- Prior experience with AI initiative tracking/governance frameworks (risk \& controls, benefits tracking)
- Familiarity with enterprise AI tooling landscape (Copilot Studio, GitHub Copilot, Claude Code, agent orchestration)
- Background in large\-scale financial services technology or security transformation programs
Orion is an equal opportunity employer, and all qualified applicants will receive consideration for employment without regard to race, color, creed, religion, sex, sexual orientation, gender identity or expression, pregnancy, age, national origin, citizenship status, disability status, genetic information, protected veteran status, or any other characteristic protected by law.
Candidate Privacy Policy
Orion Systems Integrators, LLC and its subsidiaries and its affiliates (collectively, "Orion," "we" or "us") are committed to protecting your privacy. This Candidate Privacy Policy (orioninc.com) ("Notice") explains:
- What information we collect during our application and recruitment process and why we collect it;
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Your use of Orion services is governed by any applicable terms in this notice and our general Privacy Policy.
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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Orion Innovation, 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Orion Innovation AI Hiring
Orion Innovation has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Edison, NJ, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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