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) …