Interested in this AI/ML Engineer role at Information Technology Senior Management Forum?
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Posted Date
7/14/2026
Description
Regular or Temporary:
RegularLanguage Fluency: English (Required)
Work Shift:
1st shift (United States of America)#### Please review the following job description:
This role is a Senior Product Owner in the Scaled Agile Framework who partners with product managers to balance business, technology and design priorities to deliver brand\-defining product and service experiences. The position requires the ability to understand strategic impacts but concentrates on the day\-to\-day details in order to ensure tactical execution. NOTE: In some Agile teams, there may be Product Owners from both the Business and the Tech side. In that case, the role will remain the same and there is joint accountability for the Agile team’s results. In rare instances, where the business cannot provide a PO, a Tech PO could step in to fulfill the responsibilities.
The Senior Product Owner is responsible for executing the strategy for their area of responsibility based on client and company needs, which can include client experience, back office processes or systemic processes outside the client journey. They are a key resource in backlog management for the teams. The Product Owner works hand\-in\-hand with Product Managers to translate the product vision into epics and features that can be actioned by the delivery teams. The work will span the entire delivery process; from assisting with identification of areas for improvement, to more detailed work in authoring user stories, working closely with technical leads/scrum teams to ensure the solution effectively addresses experience priorities while maintaining technical integrity, and overseeing tactical execution of efforts. The Senior Product Owner is expected to be a thought leader in delivering complex technology projects to market.
Taking a holistic perspective, this position will be responsible for delivering the experience across all relevant pieces within their assigned area of responsibility.
The Senior Product Manager – Wholesale Lending \& Sales Enablement AI Platform is responsible for defining and executing the strategy, roadmap, and delivery of Truist's Wholesale Lending \& Sales Enablement AI\-powered platform focused on automating and augmenting Wholesale Banking workflows through agentic AI, intelligent orchestration, and human\-in\-the\-loop decisioning. This role partners closely with business leaders, technology teams, data and AI specialists, risk and compliance stakeholders, and external vendors to identify high\-value use cases, prioritize investments, and deliver scalable AI capabilities that improve productivity, client outcomes, and operational efficiency. The ideal candidate combines strong product management expertise with a passion for emerging AI technologies, leading cross\-functional teams to translate business opportunities into measurable results while establishing governance, adoption, and long\-term platform growth. The role will serve as a key leader in advancing the organization's AI\-first transformation strategy and expanding the platform as an enterprise capability.
Additional Preferred Qualifications:
Consulting Background
AI Certifications / Experience
Wholesale Banking Experience \- Sales, Commercial Lending, Payments, Process Optimization
Product Management Experience
ESSENTIAL DUTIES AND RESPONSIBILITIES
Following is a summary of the essential functions for this job. Other duties may be performed, both major and minor, which are not mentioned below. Specific activities may change from time to time.
1\. May assume broad responsibility for components of a complex initiative, provide the direction or vision for a group of scrum teams related to their business solution, and/or be called upon to solve highly complex or highly technical problems.
2\. Lead their delivery team’s priorities in PI planning, sprint planning, and other agile ceremonies; aligned to larger experience platform priorities and vision defined by product management.
3\. Align with product managers to clearly articulate product strategy to the delivery team.
4\. Deliver new experiences by working directly with delivery, experience design, business, and operations partners to design new products and improvements to existing capabilities.
5\. Author and maintain the team’s backlog of user stories and serve as a subject matter expert on features, user stories, and product capabilities.
6\. Perform triage on critical issues, escalating as necessary, and communicating consistently and clearly with all concerned parties.
7\. Serve as key resource to development team to answer questions, provide clarifications, and conduct and coordinate business validation, focusing on fitness for use.
8\. Update leadership on the epic and feature delivery schedule, including dependencies impacting deliverables, along with recommended solutions.
9\. Partner with solution architects and other technical leads to ensure their solutions effectively address program priorities while balancing client experience and technical integrity.
10\. Facilitate sprint planning with stakeholder groups to drive alignment and visibility for which features will be built when, and to ensure overall adherence to the product roadmap and enterprise strategic themes.
11\. Facilitate sprint demos and provide final acceptance for completed user stories in sprint demos; ensuring that the story meets acceptance criteria and otherwise meets its definition of done.
12\. Co\-ordinate the creation of release\-specific business documents, such as support model definitions, go/no\-go approvals, internal release notes, and release\-related living documents.
13\. Risk Management: Ensure all Product Management Lifecycle (PML) process \& procedures are followed, supporting security, risk, audit, and more, and ensure action items and deadlines are met. Partner with product manager on evidence to support recommendations.
14\. Mentor other Product Owners on product delivery practices.
QUALIFICATIONS
Required Qualifications:
The requirements listed below are representative of the knowledge, skill and/or ability required. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.
1\. Bachelors’ degree in business, engineering, design, or technology field; or related field or equivalent combination of education and professional experience
2\. Ten or more years of banking, financial services, or other relevant work experience
3\. Five or more years of product, analysis, technology, and/or design experience
4\. Five or more years of leading cross functional teams
5\. Previous experience working as a Product Owner for Agile team(s)
6\. Experience defining and delivering large\-scale business initiatives to execute on a product roadmap, including stakeholder management, requirements elicitation, test planning/support and business\-level product validation
7\. Demonstrated technical acumen and an ability to work with the technology organization to align product and technology roadmaps
8\. Proven ability to translate strategic plans into tactical daily actions for execution
9\. Proven ability to lead cross\-functional teams without formal authority
10\. Comfortable managing concurrent projects in a fast\-based, results\-driven environment
11\. Comfortable with ambiguity, leading working autonomously and making independent decisions
12\. Strong analytical skills, ability to interpret data and trends, diagnose problems, and recommend action plans to resolve issues
13\. Excellent skills in presentation, facilitation, communication, and negotiation
14\. Experience in roles requiring strong communication and interpersonal skills \& the creation and delivery of succinct executive\-level presentations to explain and sell plans/vision
Preferred Qualifications:
1\. Master’s degree in business, engineering, design, or technology field; banking or financial management education
2\. Experience working with distributed teams (onshore/offshore)
3\. Certified SAFe® Product Owner/Product Manager (or equivalent)
OTHER JOB REQUIREMENTS / WORKING CONDITIONS
Sitting
Constantly (More than 50% of the time)
Standing
Frequently (25% \- 50% of the time)
Walking
Frequently (25% \- 50% of the time)
Visual / Audio / Speaking
Able to access and interpret client information received from the computer and able to hear and speak with individuals in person and on the phone.
Manual Dexterity / Keyboarding
Able to work standard office equipment, including PC keyboard and mouse, copy/fax machines, and printers.
Availability
Able to work all hours scheduled, including overtime as directed by manager/supervisor and required by business need.
Travel
Minimal and up to 10%
General Description of Available Benefits for Eligible Employees of Truist Financial Corporation: All regular teammates (not temporary or contingent workers) working 20 hours or more per week are eligible for benefits, though eligibility for specific benefits may be determined by the division of Truist offering the position. Truist offers medical, dental, vision, life insurance, disability, accidental death and dismemberment, tax\-preferred savings accounts, and a 401k plan to teammates. Teammates also receive no less than 10 days of vacation (prorated based on date of hire and by full\-time or part\-time status) during their first year of employment, along with 10 sick days (also prorated), and paid holidays. For more details on Truist’s generous benefit plans, please visit our Benefits site. Depending on the position and division, this job may also be eligible for Truist’s defined benefit pension plan, restricted stock units, and/or a deferred compensation plan. As you advance through the hiring process, you will also learn more about the specific benefits available for any non\-temporary position for which you apply, based on full\-time or part\-time status, position, and division of work.
*Truist is an Equal Opportunity Employer that does not discriminate on the basis of race, gender, color, religion, citizenship or national origin, age, sexual orientation, gender identity, disability, veteran status, or other classification protected by law. Truist is a Drug Free Workplace.*
EEO is the Law E\-Verify IER Right to Work
Type
Full\-time
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 Information Technology Senior Management Forum, 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 in Demand for This Role
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.
Information Technology Senior Management Forum AI Hiring
Information Technology Senior Management Forum has 44 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist, AI Architect, AI Safety. Positions span McLean, VA, US, San Jose, CA, US, New York, NY, US. Compensation range: $126K - $392K.
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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