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About This Role
Overview:
The Technical Product Owner is a highly visible principal\-level product leader, responsible for driving the successful delivery and adoption of enterprise AI initiatives, platforms, and solutions. This role combines product ownership, technical acumen, and program execution to translate business opportunities into scalable AI capabilities that deliver measurable value.
The role partners closely with business stakeholders, architects, engineering teams, data teams, security, governance, and platform providers to define requirements, prioritize roadmaps, manage delivery, and ensure successful implementation of AI\-powered solutions. This individual serves as the bridge between strategy and execution, ensuring AI initiatives progress from concept to production while aligning with enterprise architecture, security, and governance standards.
Responsibilities:
AI Product Strategy \& Delivery* Own the end\-to\-end delivery lifecycle for AI initiatives, platforms, and use cases.
- Partner with business stakeholders to identify, evaluate, and prioritize AI opportunities.
- Translate business objectives into actionable product roadmaps, epics, user stories, and delivery plans.
- Define MVPs, phased releases, and adoption strategies for Data \& AI solutions.
- Drive execution across multiple initiatives simultaneously while managing dependencies, risks, and timelines.
- Establish success metrics and KPIs to measure business value and platform adoption.
AI Use Case Enablement* Lead discovery, requirements gathering, and solution definition for AI use cases across the enterprise.
- Facilitate workshops with business and technology stakeholders to refine use case scope and expected outcomes.
- Support use case evaluation, feasibility analysis, and prioritization.
- Coordinate proof\-of\-concepts, pilots, and production implementations.
- Ensure AI solutions are aligned with enterprise standards, governance requirements, and business objectives.
Technical Product Ownership* Collaborate with architects and engineering teams to define solution requirements and technical capabilities.
- Develop and maintain product backlogs, user stories, acceptance criteria, and release plans.
- Internal
- Participate in architecture reviews, design discussions, and solution planning activities.
- Understand AI architecture patterns including RAG (Retrieval\-Augmented Generation), Agentic AI , Graph RAG, AI Assistants \& Copilots, Knowledge Management Platforms, Model Orchestration Frameworks
- Ensure technical requirements align with scalability, security, reliability, and operational objectives.
AWS \& Data Platform Collaboration* Partner with cloud, platform, and data engineering teams to deliver AI capabilities leveraging AWS Bedrock, Snowflake Cortex AI, Snowflake Native AI capabilities
- Coordinate platform onboarding, integration, testing, and production readiness activities.
- Drive adoption of reusable platform capabilities, patterns, and accelerators.
Stakeholder Management \& Governance* Serve as the primary liaison between business stakeholders and technology delivery teams.
- Present roadmap progress, risks, dependencies, and outcomes to leadership.
- Coordinate with Security, Risk, Compliance, Architecture Review Boards, and AI Governance teams.
- Ensure AI initiatives comply with enterprise policies and responsible AI practices.
- Manage vendor engagements, proof\-of\-concepts, and external partnerships.
Leadership \& Best Practices* Champion agile product management and iterative delivery practices.
- Drive alignment across business, architecture, engineering, and governance teams.
- Promote reuse of AI platform capabilities and enterprise standards.
- Foster collaboration, transparency, and continuous improvement.
- Stay informed on emerging AI technologies, market trends, and industry best practices.
Qualifications:
Bachelor's Degree and 8 years of experience in Product Management, Scrum, Agile OR High School Diploma or GED and 12 years of experience in Product Management, Scrum, AgilePreferred Qualifications* 8\+ years of experience in Product Management, Product Ownership, Program Delivery, Technology Delivery, or related leadership roles.
- 3\+ years of experience delivering AI, Data, Analytics, Cloud, or Digital Transformation initiatives in enterprise environments.
- Proven ability to lead complex cross\-functional programs involving business stakeholders, architecture, engineering, security, and governance teams.
- Strong understanding of Generative AI, Large Language Models (LLMs), Retrieval\-Augmented Generation (RAG), AI Agents, and emerging AI technologies.
- Hands\-on knowledge of cloud\-native architectures and services, preferably AWS, including AI/ML, data, integration, and platform capabilities.
- Experience working with Agile methodologies, backlog management, roadmap planning, user story development, release management, and product lifecycle processes.
- Excellent communication, stakeholder management, and executive presentation skills, with the ability to translate business needs into scalable technical solutions.
- Experience delivering Enterprise AI Platforms or large\-scale AI products.
- Experience with AWS Bedrock, Snowflake Cortex AI, Vector Databases, GraphRAG, and Agentic AI solutions.
- Experience within Banking, Financial Services, or other regulated industries.
- Product Owner (CSPO, SAFe POPM) and/or AWS Cloud/AI certifications.
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Benefits are an integral part of total rewards and First Citizens Bank is committed to providing a competitive, thoughtfully designed and quality benefits program to meet the needs of our associates. More information can be found at https://jobs.firstcitizens.com/benefits.
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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At First Citizens Bank, 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 $178,940 based on 11,900 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 $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
First Citizens Bank AI Hiring
First Citizens Bank has 3 open AI roles right now. They're hiring across AI Architect, AI/ML Engineer. Positions span AZ, US, Raleigh, NC, US.
Location Context
Across all AI roles, 16% (613 positions) offer remote work, while 3,187 require on-site attendance. Top AI hiring metros: New York (2,448 roles, $210,000 median); San Francisco (1,990 roles, $253,000 median); Los Angeles (1,686 roles, $189,000 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, 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 $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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