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Ericsson Inc. does not sponsor U.S work authorizations for this job position including U.S. immigration filings for initial and/or change of employer paperwork for H-1’s, H-1B1’s, E-3’s, O-1’s, and TN’s. Ericsson also does not hire F-1’s working on CPT or EAD for this position.
About this opportunity
Ericsson is looking for a strategic, high-impact enterprise professional to join our global Data & AI program as AI Strategy & Portfolio Lead. Based in Plano, Texas, this high-visibility role combines AI architecture and solution expertise with transformation experience — connecting senior business stakeholders to compelling data and AI solutions and leading those solutions through delivery, adoption, and measurable impact. The role spans Support (HR, Finance, Sourcing, and Customer Support), Marketing, Product Development, and Network and Cloud domains, helping leaders translate strategic ambition into funded, executable change.
What you will do:
- Serve as a strategic, commercially-aware partner for senior business and technology leaders — translating ambiguous challenges into opportunities, roadmaps, and investment recommendations.
- Build a compelling enterprise narrative for data and AI; generate adoption of reusable capabilities, shared platforms, and common patterns.
- Lead or guide target-state architectures and end-to-end solution designs across data, AI, cloud, integration, security, governance, and operating models.
- Convert business needs into practical solution blueprints, MVPs, roadmaps, and value cases that balance feasibility, scalability, cost, risk, and user adoption.
- Oversee the full journey from opportunity identification through mobilization, delivery, adoption, and sustained value realization.
- Define and track measurable outcomes, benefits, adoption rates, spend, and delivery milestones — using evidence to refine priorities and scale what works.
- Own and prioritize the portfolio of data and AI initiatives; lead business case development, ROI/TCO modeling, and executive recommendations.
- Evaluate vendors, platforms, and strategic partners; develop practical commercial arrangements and sourcing recommendations.
The skills you bring:
- Demonstrated experience in enterprise architecture, solution architecture, technology strategy, product leadership, consulting, or large-scale transformation delivery.
- Strong track record shaping and delivering data, AI, automation, cloud, or digital transformation solutions across complex enterprise environments.
- Proven ability to influence senior stakeholders, facilitate difficult decisions, and build alignment without relying solely on formal authority.
- Practical understanding of data, cloud, AI, integration, security, responsible AI, and operating-model considerations — with enough technical depth to evaluate trade-offs.
- Strong commercial acumen: ROI/TCO modeling, investment prioritization, benefits realization, and build/buy/reuse decisions.
- Typically 10+ years of experience in consulting, architecture, technology, product, or enterprise transformation roles with regular senior-leader exposure.
- Bachelor’s or Master’s degree in Business, Engineering, Computer Science, or a related field — or equivalent professional experience.
- AI is becoming core to how we build and deliver at Ericsson. We’re seeking talent who are ready to learn, adapt, and develop the skills needed to thrive in an AI-enabled future.
Why join Ericsson?
At Ericsson, you´ll have an outstanding opportunity. The chance to use your skills and imagination to push the boundaries of what´s possible. To build solutions never seen before to some of the world’s toughest problems. You´ll be challenged, but you won’t be alone. You´ll be joining a team of diverse innovators, all driven to go beyond the status quo to craft what comes next.
What happens once you apply?
Click Here to find all you need to know about what our typical hiring process looks like.
Ericsson uses a merit-based hiring approach that values people with different experiences, perspectives and skillsets. We truly believe this approach drives innovation, which is essential for our future growth. We encourage people from all backgrounds to apply and realize their full potential as part of our Ericsson team. Ericsson is proud to be an Equal Opportunity employer, learn more.
If you need assistance or to request an accommodation due to a disability, please contact Ericsson at [email protected].
DISCLAIMER: The above statements are intended to describe the general nature and level of work being performed by employees in this position. They are not an exhaustive list of all responsibilities, duties and skills required for this position, and you may be required to perform additional job tasks as assigned.
Primary country and city: United States (US) || Plano (Country/ City)
Job details: Strategy Management
Compensation and Benefits at Ericsson
At Ericsson, we know that our people are the key to our success. We offer a competitive package to help with your individual needs and goals.
Your Pay
The salary range for this position is dependent on various factors including, but not limited to, location, and the candidate’s combination of job-related knowledge, qualifications, skills, education, training, and experience.
Short-Term Variable Compensation Plan : Your pay also includes the opportunity for an annual bonus. Actual bonus payouts are based on performance of the business against the unit’s objectives, individual performance, and the individual bonus target. Certain eligibility and pro-ration rules apply.
Your Health
Ericsson offers excellent health benefits including the choice of three medical plan options and a dental plan option that allow an employee to select the level of coverage that suits their needs. Employees will receive company credits in an amount equal to the cost that Ericsson pays toward the cost of their medical and dental premiums for themselves and eligible covered dependents.
Your Financial Security
We invest in both your short and long-term financial wellbeing. The Ericsson US 401(k) Plan offers an automatic 3% company contribution and Ericsson match $1 for every $1 you put into the 401(k) Plan on the first 3% of your eligible pay, plus 50 cents on every $1 on the next 2% of eligible pay. When you contribute at least 5% of eligible pay, you are receiving Ericsson’s full matching contributions of 4%. Matching and company automatic contributions stop when your total eligible pay for the year reaches the IRS limits. Employees will also receive company credits in an amount equal to the cost of basic life insurance and basic accidental death and dismemberment coverage, as well as short-term and long-term disability coverage. Employees also have the option to participate in Ericsson’s Stock Purchase Plan.
Your Time
Your work-life balance is important to us. New employees are provided a minimum of 15 days of accrued vacation, up to 3 personal days per year, 11 annual holidays, 8 hours of volunteer time, and 80 hours of sick time annually. Please note paid time off is pro-rated based on the employee’s start date. Furthermore, Ericsson provides up to 16 weeks of paid maternity leave and 6 weeks of parental or adoption leave at 100% of pay.
Additional Benefits
Ericsson offers many other company-paid benefits such as financial wellness programs, educational assistance, matching gifts, and recognition programs.
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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Ericsson, 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 $212,000 based on 11,857 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Ericsson AI Hiring
Ericsson has 4 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Reading, England, UK, Austin, TX, Boise, ID, US.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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