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The Artificial Intelligence Product and Portfolio Manager leads the intake, prioritization, shaping, and value management of artificial intelligence initiatives across the enterprise. This role translates AI strategy and business demand into a prioritized, value\-driven portfolio of opportunities that are clearly defined, aligned to business outcomes, and prepared for effective delivery.
The role sits at the intersection of business, operations, governance, and technology. It partners with senior business and operations leaders to clarify root problems, define desired outcomes, and shape practical AI\-enabled opportunities. It also works closely with AI leadership, the AI Architect, AI Delivery leadership, data partners, governance stakeholders, and existing product teams to move AI ideas from initial demand signals into a disciplined, transparent portfolio.
This role helps Allied advance from traditional operating models that rely on complex rules\-based systems, manual workarounds, and proportional headcount growth toward AI\-enabled digital operating models that scale through intelligent systems, automation, human oversight, operational feedback, and continuous improvement. It provides the business framing, prioritization discipline, stakeholder alignment, delivery readiness, portfolio visibility, and value realization needed to ensure AI investments are purposeful, measurable, scalable, and aligned to organizational priorities.
Job Duties and Responsibilities:
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Manage AI Intake, Portfolio Prioritization, and Sequencing – 30%
- Establish and manage structured intake process to capture, evaluate, and prioritize AI opportunities across the enterprise.
- Assess initiatives based on business value, strategic alignment, feasibility, risk, governance considerations, dependencies, delivery capacity, and readiness.
- Maintain portfolio visibility into the AI opportunity pipeline, active initiatives, overlaps, tradeoffs, sequencing decisions, and portfolio health.
- Prepare portfolio recommendations and decision\-support materials for AI leadership, AI Governance Committee., product leaders, and executive stakeholders.
Shape Business Problems into Delivery\-Ready AI Opportunities – 25%
- Partner with senior business and operations leaders to move from broad AI requests or vague ideas to clearly defined problem statements, desired outcomes, target users, value hypotheses, and success metrics.
- Identify the root business or operational problem and ensure proposed AI initiatives are grounded in measurable business needs.
- Prepare prioritized opportunities for handoff to architecture and delivery teams, including scope, stakeholders, assumptions, dependencies, risks, expected outcomes, and initial future\-state workflow considerations.
- Partner with the AI Architect, AI Delivery leadership, data partners, and business stakeholders to refine initiatives before proof of concept or execution planning begins.
Manage AI Portfolio Visibility and Value Realization – 20%
- Define and maintain portfolio\-level measures for expected business outcomes, realized value, adoption, initiative status, and operational impact.
- Track value and outcome data from delivered AI initiatives to inform future prioritization, sequencing, and investment decisions.
- Communicate expected and realized value to AI leadership, governance stakeholders, product leaders, and executive audiences.
- Incorporate value, adoption, performance, and stakeholder feedback into recommendations about portfolio direction.
Integrate Governance, Risk, and Cross\-Functional Alignment – 15%
- Ensure legal, risk, security, compliance, responsible AI, and governance considerations are reflected in initiative evaluation, prioritization, and delivery readiness.
- Partner with the AI Governance Lead, AI Governance Committee, Information Security, Legal, Risk, Compliance, business stakeholders, and delivery partners to support coordinated portfolio decisions.
- Surface gaps, dependencies, duplicate efforts, risks, and decision points requiring cross\-functional alignment.
- Help coordinate a hybrid AI operating model across business, governance, product, data, architecture, and delivery stakeholders.
Advance AI Portfolio Management Maturity and Continuous Improvement – 10%
- Ensure legal, risk, security, compliance, responsible AI, and governance considerations are reflected in initiative evaluation, prioritization, and delivery readiness.
- Partner with the AI Governance Lead, AI Governance Committee, Information Security, Legal, Risk, Compliance, business stakeholders, and delivery partners to support coordinated portfolio decisions.
- Surface gaps, dependencies, duplicate efforts, risks, and decision points requiring cross\-functional alignment.
- Help coordinate a hybrid AI operating model across business, governance, product, data, architecture, and delivery stakeholders.
Qualifications (Education, Experience, Certifications \& KSA):
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- Bachelor’s degree or equivalent combination of education and experience; master’s degree preferred.
- 8\-10 years of work\-related experience.
- Strong product and portfolio management fundamentals Experience structuring intake, prioritization, backlog management, portfolio visibility, and decision frameworks across multiple initiatives, value streams, or stakeholder groups.
- Ability to translate ambiguous business problems into clear opportunities Skilled at moving from broad AI requests or vague ideas to well\-defined problem statements, desired outcomes, target users, success metrics, and value hypotheses.
- Stakeholder management and executive communication Comfortable working with senior business and operations leaders, facilitating alignment, and communicating priorities, tradeoffs, sequencing decisions, expected value, and realized outcomes clearly.
- Value framing and business case development Ability to define expected outcomes, quantify impact such as efficiency, cost, revenue, risk reduction, quality, customer experience, employee experience, scalability, or operational capacity, and articulate why initiatives matter.
- Practical AI literacy Working understanding of AI, machine learning, generative AI, intelligent automation, and emerging AI capabilities sufficient to evaluate opportunities, ask informed questions, assess feasibility at a high level, and partner effectively with technical teams.
- Cross\-functional collaboration Ability to work effectively with AI leaders, AI architects, AI delivery teams, engineers, data teams, governance partners, existing product managers, and business stakeholders to move initiatives from idea to delivery readiness.
\#LI\-ID1
*The above statements are intended to describe the general nature and level of work being performed by people assigned to this job. They are not intended to be an exhaustive list of all responsibilities, skills, efforts or working conditions associated with a job.*
We offer our employees a robust compensation package! Our comprehensive benefits include: medical, dental and vision insurance coverage; 100% company\-paid life and disability coverage, 401k options with company match, three weeks PTO by the end of the first year and much more. Allied proudly promotes from within as part of a strong commitment to providing career growth opportunities for employees of all levels. Our diverse business portfolio allows employees broad career options with the advantage of staying with the same organization.
All qualified candidates will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability status, protected veteran status, or any other characteristic protected by law.
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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Allied Solutions, 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Allied Solutions AI Hiring
Allied Solutions has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Carmel, IN, US.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, 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 $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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