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About This Role
Job Function: Services The role: Job Summary
The Portfolio Manager, Cloud, Data \& AI owns SoftwareOne’s cloud, data, analytics and AI service offers within the Product Development \& Readiness (PDR) team, serving as the accountable owner and primary point of coordination from intake through design, pilot, launch and ongoing portfolio review.
This role transforms cloud, data and AI concepts into market\-ready, easy\-to\-sell services with clear value propositions, delivery readiness, commercial viability and strong alignment to customer demand. The portfolio includes cloud modernization, migration and managed cloud services; data platform and analytics offers; AI advisory and adoption services and related governance, and security.
The role works closely with Cloud, Data \& AI practice SMEs, product marketing, finance, legal, sales and delivery teams to ensure offers are well\-designed, validated through pilot phase, and successfully launched into the market. It is a key execution role that connects technology strategy, market signal, customer need and delivery readiness.
SoftwareOne is an AI‑forward company. We actively use AI across our business to improve productivity, decision‑making, and outcomes \- and we are intentional about hiring people who are curious, hands\-on, actively apply AI, and lead by example as technology continues to evolve.
Role \& Responsibilities
The Portfolio Manager, Cloud, Data \& AI carries end\-to\-end responsibility for offer development and lifecycle management across cloud, data, analytics and AI services. Responsibilities map directly to the PDR workflow:
Intake Design MVP Pilot Full Launch Portfolio Review.
Cloud, Data \& AI offer ownership \& strategy
- Own the Cloud, Data \& AI portfolio, including cloud modernization, migration, managed cloud, data platforms, analytics, and AI.
- Assess new ideas for fit against cloud, data and AI market demand, customer pain points, delivery capability, partner alignment and commercial potential.
- Use portfolio performance data, customer signal, and emerging technology trends to prioritize, refresh, bundle or sunset offers.
Offer design, scope, packaging \& pricing
- Own offer design, scope, packaging and pricing for Cloud, Data \& AI services, ensuring each offer is simple to sell, scalable and differentiated in the market.
- Define target buyers, use cases, business outcomes and value propositions.
- Partner early with Product Marketing, Finance, Legal and delivery leaders to validate positioning, naming, commercial guardrails, margin, delivery assumptions and launch readiness.
Build, pilot \& launch (the gates)
- Guide MVP content, delivery playbooks, sales enablement and consultant readiness.
- Manage pilot delivery and the client relationship; secure 2–3 pilots sold before advancing offers to full launch.
- Co\-own the pilot gate go/no\-go decision incorporating customer feedback, technical feasibility, security considerations and commercial performance.
- Make fast pivot\-or\-scrap calls based on pilot outcomes, and market readiness.
Cross\-functional \& stakeholder leadership
- Build and maintain strong relationships with Cloud, Data \& AI practice teams, CoEs, and sales leaders.
- Provide Cloud, Data \& AI portfolio performance insights into the portfolio review and RLT reporting cadence.
- Travel up to 10%
What we need to see from you: What you offer
- Proven experience in product management, portfolio management, offer management or solution development within cloud, data, analytics, AI, software or technology services.
- Strong commercial acumen with experience shaping packaged services, pricing models, margins, value propositions and business cases for technology solutions.
- Proven ability to build trusted relationships and drive collaboration across teams.
- Comfort using performance data, customer feedback, market insights and technology trends to make invest, refresh, bundle or sunset decisions.
- Domain depth, or the ability to ramp quickly, across cloud platforms, data platforms, analytics, AI adoption, GenAI, Copilot, responsible AI, governance, security and automation.
- Entrepreneurial, accountable, customer\-focused and comfortable operating in a fast\-moving cloud, data and AI market.
- Aligned to SoftwareOne’s IMPACT values.
*The preceding job profile has been designed to indicate the general nature and level of work performed by associates within this role. It is not designed to contain or be interpreted as a comprehensive inventory of all duties, responsibilities, and qualifications required. Additional duties may be assigned and may be subject to change at any time due to reasonable accommodation or other reasons.*
Success Criteria
- A healthy, well\-governed pillar where every offer has a clear owner and lifecycle stage.
- Disciplined movement through the gates: no offer reaches full launch without 2–3 pilots sold.
- Fast, clear pivot\-or\-scrap decisions instead of ambiguous review cycles.
- Offers that evolve with market demand, informed by portfolio intelligence and customer signal.
- Strong attach, conversion and revenue contribution from the pillar, visible on the RLT dashboard.
Organizational Alignment
- Reports to Senior Director, Product Development \& Readiness
- Works closely with Senior Director, PDR, Sr. Manager Portfolio Strategy \& Intelligence, Portfolio Operations Manager, and Director of Product Marketing
- Collaborates internally with Service SMEs \& CoEs, Sales Leadership \& Enablement, Finance, Legal, and Regional Leadership Team (RLT)
- Engages externally with existing customers, prospective customers, ISV and technology partners, channel partners, hyperscalers (Microsoft, AWS, Google Cloud), industry analysts, and Global PMM / GTM teams
What we offer
- Generous pay with bonus structure (quarterly or bi\-annual depending on the role)
- Independent environment without a lot of red tape where you are empowered to make decisions
- Substantial benefits package that includes:
- Full suite of medical coverage with A\+ carriers, Dental, and Vision with strong employer contributions plus additional voluntary coverage available for Pets, Identity Theft Protection, Accident \& Critical Illness
- 401k program with employer matching 50% up to the first 10% of employee’s contributions
- Wellness plan that includes credits to premiums and employer contributions towards the savings plan of your choice
- Access to EAP and concierge services plus pre\-paid legal at no cost
- Abundant time off that includes paid holidays, floating holidays, your birthday off, a volunteer day, and discretionary time off (DTO)
- Employee stock purchase plan
- Learning and development opportunities galore, tuition reimbursement, and much more!
- Specific to Nashville and Milwaukee\-based office employees: company\-paid parking
- Winning culture, inclusive environment, and friendly people all over the world
- A remote\-friendly organization, with colleagues working remotely either part or full\-time
Target compensation for this role will be $150K \- 165K USD (mix of base salary and bonus). Actual offers may be higher or lower than this range and will be determined based on a variety of factors, including (but not limited to) candidates’ qualifications, experience, education, and work location.
We are not able to consider candidates residing in the state of Hawaii currently.
Why SoftwareOne?:
SoftwareOne and Crayon have come together to form a global, AI\-powered software and cloud solutions provider with a bold vision for the future. With a footprint in over 70 countries and a diverse team of 13,000\+ professionals, we offer unparalleled opportunities for talent to grow, make an impact, and shape the future of technology. At the heart of our business is our people. We empower our teams to work across borders, innovate fearlessly, and continuously develop their skills through world\-class learning and development programs. Whether you're passionate about cloud, software, data, AI, or building meaningful client relationships, you’ll find a place to thrive here. Join us and be part of a purpose\-driven culture where your ideas matter, your growth is supported, and your career can go global.
Accommodations:
SoftwareOne welcomes applicants from all backgrounds and abilities to apply. If you require reasonable adjustments at any point during the recruitment process, email us at [email protected]. Please include the role for which you are applying and your country location. Someone from our organization that is not part of the decision\-making process will be in touch to discuss your specific needs, and we will make every effort to accommodate you. Any information shared will be stored securely and treated in the strictest of confidence in line with GDPR.
At SoftwareOne, we are committed to providing an environment of mutual respect where equal employment opportunities are available to all applicants and teammates without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local laws. Additionally, we encourage experienced individuals that have taken an intentional career break and are now prepared to return to work to explore our SOAR program
Salary Context
This $150K-$165K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 roles with salary data).
View full AI/ML Engineer salary data →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 SoftwareOne, 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($157K) sits 28% below the category median. Disclosed range: $150K to $165K.
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.
SoftwareOne AI Hiring
SoftwareOne has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Nashville, TN, US, US. Compensation range: $140K - $165K.
Location Context
AI roles in Austin pay a median of $214,343 across 87 tracked positions.
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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