Interested in this AI/ML Engineer role at Globant?
Apply Now →About This Role
Globant is one of the fastest\-growing tech brand in the world, achieving $2\.3B in revenue and a
29\.9% compound annual growth rate. North America is the biggest market for Globant since its
beginnings and the Consumer Products \& Retail sector is one of the top industries across the
US and Canada.
As we continue this incredible journey, now is the perfect time to join our team in North America,
where amazing opportunities lie ahead. As a Microsoft Partner, we help our clients leverage the
Globant Studios to drive growth and innovation in the Consumer Products \& Retail sectors.
Our mission is to serve businesses across the region, enabling them to reach new heights
through robust Microsoft strategies and solutions
Key Responsibilities:
Solution Ownership \& Delivery:
- Engage with Consumer Products and Retail AI industry leaders as well as AI and Globant top
accounts for co\-sell motions.
- Make good use of AI funding programs and Marketing co\-funds to impact AI
Globant business.
- Led the AI Studio go\-to\-market strategy in Consumer Products \& Retail for North
America.
- Act as the lead solution owner for our AI offerings, managing projects across the AI
- Engage directly with clients to understand their needs, offering customized AI
solutions that drive impactful outcomes.
Customer\-Focused Business Development:
- Serve as a trusted advisor for clients during pre\-sales and sales stages, positioning our
AI capabilities in the Consumer Products \& Retail space as integral to their business
growth.
- Lead efforts in identifying and cultivating farm opportunities to deepen client
relationships.
- Partner with sales teams to develop tailored solutions that align with client objectives and
industry demands.
Portfolio Growth \& Go\-To\-Market Strategy:
- Contribute to the go\-to\-market strategies to drive the growth within
North America in Consumer Products \& Retail Services industry.
- Collaborate with the VP of Tech, Managing Director, VP of Delivery for the portfolio, and
the SVP of Globant Europe Enterprise Business Network to coordinate sales
strategies.
- Bring innovative ideas and solutions to enhance our offerings, ensuring our
services meet evolving market needs.
Team Collaboration \& Leadership:
- Lead by example, fostering a collaborative environment within the CPG\-Retail Studio and
mentoring team members across AI projects.
- Work as part of the VP of Tech team, contributing to strategic initiatives and aligning with
broader organizational goals.
- Promote cross\-functional collaboration, leveraging insights from other Globant Studios,
such as AI Consumer Products \& Retail, Process Optimization and Cloud Studios, to
enhance project outcomes and impact Globant client’s business.
Qualifications:
- Extensive experience in AI solutions.
- Proven success in presales, sales, and solution delivery, with a customer\-centric
approach.
- Strong leadership and collaborative skills with experience in managing teams, staffing for
projects, and motivating colleagues.
- Ability to work with cross\-functional teams and stakeholders to achieve business
objectives.
- Skilled in developing and executing growth strategies within the AI ecosystem, with a
strong understanding of the Spanish market.
Benefits for this position will likely include the following:
- Unlimited paid time off
- Healthcare benefits
- Dental benefits
- Vision benefits
- Disability insurance
- Life insurance benefits
- Retirement benefits, including 401(k) and a potential company match.
- Parental leave for both birthing (up to 14 weeks of paid leave) and non\-birthing
parents (up to 4 weeks paid leave), as well as adoption leave (up to 8 weeks of paid
leave), subject to qualifications
- Child hospitalization leave (up to 15 paid calendar days per year), subject to
qualifications
- “Be kind to yourself day” on your anniversary (subject to terms and conditions)
- Graduation and birthing gifts, subject to eligibility
- Paid Jury duty (up to 4 weeks a year), subject to eligibility
- Paid moving days (up to 2 days a year), subject to eligibility
- Approximately 12 paid company holidays a year
Accommodations:
If you require accommodation, please contact the recruiting team
Applicant Privacy:
Globant will collect and process your information as part of this application. For more
information, review Globant’s Privacy Policy.
Equal Employment Opportunity:
Globant is an equal opportunity and affirmative action employer. We are committed to building a
workforce representative of the users we serve, fostering a culture of belonging, and providing
equal employment opportunity regardless of any basis protected by law.
Use of AI in Recruitment:
Globant may use AI and machine\-learning technologies in the recruitment process. All qualified
applicants will receive consideration for employment without regard to race, color, religion, sex,
national origin, disability, veteran status, or any other protected characteristic. Globant is
committed to providing reasonable accommodations for qualified individuals with disabilities.
Remote Work:
Remote work permissibility may change based on business needs. For questions regarding
work locations, speak directly with your hiring partner.
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 Globant, 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.
Globant AI Hiring
Globant has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in FL, 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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