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About This Role
itD is a leading, woman- and minority-owned global technology consulting company. We are hiring an AI Native Engineer to join our diverse and dynamic global team. This role is fitting for someone who excels in an independent yet collaborative work environment, while meeting deadlines.
The role is 100% remote, with expectations to support across different timezones.This is a W2 opportunity with itD and contracted at a Fortune 500 client in Silicon Valley. The individual selected will be instrumental in helping us continue to deliver excellence to our base of leading global accounts.
You will also interact closely with itD's Communities of Practice (CoP), expand your network, and grow your career. This is a unique chance to meet others who think differently and are passionate about challenging the status quo!
Internal Responsibilities
- Attend regular internal practice community meetings.
- Collaborate with your itD practice team on industry thought leadership.
- Complete client case studies and learning material (blogs, media material).
- Build out material to contribute to the Digital Transformation practice.
- Attend internal itD networking events (in person and virtual).
- Work with leadership on career fast-track opportunities.
Job Title: AI Native Engineer Location: Remote USA Duration: 6 months Compensation: DOE + full benefits Interview: 3 Rounds, Webex video Years of Experience: 5yrs+
Job Summary
About the Role
The client's IT M&A team integrates acquired companies into their business systems and processes, with a focus on getting acquired products sold, fulfilled, billed, and recognized as revenue quickly and accurately. We are looking for an AI Native Engineer who combines hands-on engineering skill with deep Opportunity to Cash (O2C) knowledge.
"AI native" means you default to AI-assisted and agentic approaches when designing, building, testing, and operating solutions. You use LLMs, copilots, and automation as core parts of your workflow and as components of the systems you ship. You will help the M&A team reduce integration timelines, automate data and process mapping, and improve the quality of O2C readiness for every acquisition.
Key Responsibilities
- Design and build AI-driven integration solutions that accelerate O2C onboarding for acquired entities, including product and pricing setup, order flows, billing, invoicing, collections, and revenue recognition handoffs.
- Develop agentic workflows and LLM-powered tools for tasks such as mapping source-system data to target schemas, analyzing contracts and SKUs, generating integration requirements, and validating configuration.
- Automate data migration and reconciliation across customer, product, contract, order, and financial data, using AI to detect anomalies, duplicates, and mapping gaps.
- Partner with Finance, Sales Operations, Legal, Revenue Accounting, and Business Architecture to translate O2C requirements into scalable technical solutions.
- Assess acquired companies' O2C landscapes during diligence and integration planning, identifying gaps, risks, and automation opportunities.
- Build and maintain APIs, integrations, and data pipelines connecting CRM, CPQ, order management, ERP, billing, and subscription platforms.
- Apply AI-assisted development practices (code generation, automated testing, documentation, and code review) to raise delivery speed and quality.
- Establish guardrails and governance for AI use, including data privacy, security, audit-ability, model evaluation, and compliance with SOX and financial controls.
- Create reusable accelerators, such as playbooks, templates, prompt libraries, and integration patterns, that scale across multiple M&A deals.
- Support go-live and hypercare, resolving defects and process breaks across the order-to-revenue chain.
Required Qualifications
- Bachelor's degree in Computer Science, Engineering, Information Systems, or a related field (or equivalent experience).
- 5+ years of software or integration engineering experience in an enterprise environment.
- Experience with cloud platforms (AWS, Azure, GCP), CI/CD, and infrastructure as code.
- Experience building evaluation frameworks for AI systems and monitoring them in production.
- Hands-on experience building with LLMs and generative AI (prompt engineering, RAG, tool use, agent frameworks, or fine-tuning) and applying AI tools throughout the development lifecycle.
- Strong proficiency in Python and/or Java, JavaScript/TypeScript, plus SQL and experience with REST/event-driven APIs.
- Experience with enterprise platforms such as Salesforce, SAP, Oracle, or similar CRM, ERP, and billing systems.
- Experience with data integration, ETL/ELT, and data quality practices.
- Strong communication skills and the ability to work with both technical and business stakeholders.
What Success Looks Like
- Faster integrations: measurable reduction in time to O2C readiness for acquired businesses.
- Higher accuracy: fewer order, billing, and revenue defects after go-live.
- Scalable assets: AI accelerators and reusable patterns adopted across multiple deals.
- Trusted partnership: strong relationships with Finance, Sales Ops, and IT stakeholders.
Core Competencies
- AI-first problem solving and continuous learning
- Systems thinking across the end-to-end order-to-revenue chain
- Ownership and bias for action in fast-moving, ambiguous environments
- Risk awareness and a controls-minded approach
- Collaboration across global, cross-functional teams
Preferred qualifications and skills
- Direct experience with M&A integration or divestiture programs, including Day 1 readiness and post-merger consolidation.
- Working knowledge of the Opportunity to Cash / Quote to Cash lifecycle: opportunity and quote management, CPQ, contract and order management, fulfillment, billing and invoicing, subscription and renewals, collections, and revenue recognition.
- Familiarity with revenue accounting standards (ASC 606), tax and compliance considerations, and financial controls (SOX).
- Experience with subscription, SaaS, and hybrid hardware/software/services business models.
- Knowledge of process mining, workflow orchestration, or low-code/no-code automation tools.
- Relevant certifications (e.g., Salesforce, SAP, cloud, or AI/ML).
- Tech and/or Enterprise, Fortune 500 companies
- Cisco, Splunk, Meta, Google, Microsoft, ServiceNow, et al.
Education
- Bachelor's degree in Computer Science, Engineering, Information Systems, or a related field (or equivalent experience)
Company Description
About itD: We are part of a new generation of consulting and software development company that blends diversity, innovation, and integrity with real business results. Our structure rejects any strong hierarchy, empowering us to deliver excellent results. We are a woman- and minority-led firm. Every day, we challenge ourselves to be considerate, fair and to re-think what great outcomes mean for our customers. This permeates down to how we approach every interaction, on every project, for every client. You'll thrive here if you are a dynamic self-starter, a difference-maker or someone who wants to deliver great results, without constraints.
The itD Digital Experience: Joining us means you'll be part of our global community, you have a say about your own career journey, and you'll get a chance to give back to causes that matter. You will experience working with Fortune 500 companies and high-performance teams across numerous industries. itD offers our employees excellent benefits such as medical, dental, vision, life insurance, paid holidays, 401K + matching, networking & career learning and development programs. We are growing and we want to see you grow! Visit https://itdtech.com/careers to learn more about what working at itD can mean for you.
All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability or protected veteran status, or any other legally protected basis, in accordance with applicable law. itD is committed to working with and providing reasonable accommodation to individuals with disabilities. If, because of a medical condition or disability, you need a reasonable accommodation for any part of the application process, or to perform the essential functions of a position, please contact us at [email protected] and let us know the nature of your request and your contact information.
Additional Info
Dynamic environment in a culture of respect, empowerment and recognition for a job well done, apply today!
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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At itD Tech, 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 $212,040 based on 13,780 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
itD Tech AI Hiring
itD Tech has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Las Vegas, NV, US.
Remote Work Context
Remote AI roles pay a median of $188,200 across 2,430 positions. About 6% 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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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