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About This Role
Who We Are
At Kyndryl, we run and reimagine the mission\-critical technology systems that drive advantage for the world’s leading businesses. We are at the heart of progress; with proven expertise and a continuous flow of AI\-powered insight, enabling smarter decisions, faster innovation, and a lasting competitive edge. For our people—Kyndryls—that means doing purposeful work that powers human progress. Join us and experience a flexible, supportive environment where your well\-being is prioritized and your potential can thrive.
The Role
Your role
The Associate AI Engineer is an entry\-level technical role supporting the design, development, deployment, and optimization of artificial intelligence, machine learning, analytics, and data\-driven solutions. Working alongside experienced AI Engineers, Data Engineers, Data Scientists, Solution Architects, and business stakeholders, you will develop foundational skills in modern AI technologies, cloud platforms, software engineering, data engineering, analytics, and responsible AI practices.
Join Kyndryl and gain the trust, autonomy and teamwork to help define what comes next.
This role offers two areas of specialization within a common AI Engineering career framework:
AI Engineering Path
Build, test, and deploy AI\-powered applications, generative AI solutions, intelligent agents, and machine learning capabilities that help solve business challenges and improve user experiences.
Data Engineering \& Analytics Path
Prepare, manage, and optimize the data foundations that power AI, machine learning, analytics, and business intelligence solutions.
Candidates may begin in one focus area but will gain exposure to both disciplines as they develop their careers.
What You'll Do
Core Responsibilities (All Associates)
- Collaborate with technical teams and business stakeholders to understand requirements and support AI\-enabled business outcomes.
- Contribute to the design, development, testing, deployment, and support of AI and data solutions.
- Participate in agile development practices, code reviews, troubleshooting, and continuous improvement activities.
- Document technical solutions, data sources, workflows, assumptions, and operational procedures.
- Apply security, governance, privacy, compliance, and responsible AI standards in all work.
- Build expertise in modern cloud, AI, and data technologies through hands\-on learning and project experience.
AI Engineering Focus
- Support the development of AI, machine learning, and generative AI applications.
- Assist in building intelligent agents, conversational experiences, retrieval\-augmented generation (RAG) solutions, and AI\-assisted workflows.
- Develop and maintain application components, APIs, integrations, and automation capabilities.
- Help evaluate model performance, response quality, scalability, and user experience.
- Participate in testing, monitoring, and optimization of AI solutions.
- Learn modern AI frameworks, cloud AI services, and software engineering practices.
Data Engineering \& Analytics Focus
- Collect, cleanse, transform, validate, and prepare structured and unstructured data for AI and analytics solutions.
- Use SQL, Python, and approved platform tools to query, manipulate, and analyze data.
- Assist with exploratory data analysis to identify patterns, trends, insights, and data quality issues.
- Support the creation of data workflows, reusable scripts, data pipelines, and data preparation processes.
- Help prepare training data, features, and evaluation datasets for machine learning and AI initiatives.
- Contribute to data governance, quality, lineage, and operational excellence practices.
Who You Are
Required skills and experience
- Bachelor's degree in Computer Science, Data Science, Information Systems, Engineering, Mathematics, Statistics, or a related discipline, or equivalent practical experience.
- 0\-3 years of experience in software engineering, AI, machine learning, data engineering, analytics, or related technical fields.
- Working knowledge of Python or a similar programming language.
- Basic understanding of software development principles and problem\-solving techniques.
- Familiarity with data structures, databases, APIs, cloud technologies, or modern development tools.
- Strong analytical, communication, collaboration, and documentation skills.
- Demonstrated curiosity and interest in emerging AI and data technologies.
Preferred Qualifications
- Exposure to Microsoft Azure, Azure AI Services, Azure OpenAI, AWS, Google Cloud, or similar platforms.
- Familiarity with AI, machine learning, generative AI, large language models, retrieval systems, or intelligent automation.
- Exposure to SQL, data analysis, data preparation, ETL/ELT, or data pipeline concepts.
- Experience with Git, CI/CD, Docker, DevOps, MLOps, or software engineering practices.
- Knowledge of visualization, reporting, or business intelligence tools.
- Relevant certifications in AI, cloud, data, or software engineering technologies.
- Understanding of responsible AI, data privacy, security, and governance principles.
Key Skills
Common Skills
- Problem solving and analytical thinking
- Python programming
- Cloud platform fundamentals
- Technical documentation
- Collaboration and communication
- Responsible AI and technology governance
AI Engineering Skills
- Generative AI and large language model fundamentals
- AI application development
- Intelligent agents and automation
- Prompt engineering and AI solution testing
- APIs and system integration
- Machine learning concepts
Data Engineering \& Analytics Skills
- SQL and data querying
- Data preparation and transformation
- Data quality and validation
- Exploratory data analysis
- Data pipelines and workflows
- Analytics and business insights generation
What Success Looks Like
As an Associate AI Engineer, you will:
- Deliver high\-quality work with increasing independence and technical proficiency.
- Develop practical AI, software, and data engineering capabilities.
- Contribute to solutions that improve business outcomes and user experiences.
- Demonstrate sound judgment, curiosity, and continuous learning.
- Collaborate effectively across multidisciplinary teams.
- Consistently apply security, governance, privacy, and responsible AI practices in your work
The compensation range for the position in the U.S. is \- $63,360 to $114,120 based on a full\-time schedule. Your actual compensation may vary depending on your geography, job\-related skills and experience. For part time roles, the compensation will be adjusted appropriately. The pay or salary range will not be below any applicable state, city or local minimum wage requirement.
There is a different applicable compensation range for the following work locations:
California (San Francisco Bay Area) :$76,080 to $137,040
California (All Other): $69,720 to $125,640
Colorado: $63,360 to $114,120
Massachusetts/Virginia: $63,360 to $125,640
New York City: $76,080 to $137,040
Washington: $69,720 to $125,640
Washington DC: $69,720 to $125,640
This position will be eligible for Kyndryl’s discretionary annual bonus program, based on performance and subject to the terms of Kyndryl’s applicable plans. You may also receive a comprehensive benefits package which includes medical and dental coverage, disability, retirement benefits, paid leave, and paid time off. Note: If this is a sales commission eligible role, you will be eligible to participate in a sales commission plan in lieu of the annual discretionary bonus program.
Applications will be accepted on a rolling basis.
Being You
The “Kyn” in Kyndryl means kinship, which represents the strong bonds we have with each other, our customers and our communities. We focus on ensuring all Kyndryls feel included and we welcome people of all cultures, backgrounds, and experiences. Even if you don’t meet every requirement, we encourage you to apply. We believe in growth, and we’re excited to see what you can bring. At Kyndryl, employee feedback has told us that our number one driver of employee engagement is belonging. That sense of belonging — being a valued, respected, trusted member of the team — is fundamental to our culture and fueling great experiences for our customers. This dedication to welcoming everyone into our company means that Kyndryl gives you the ability to thrive and contribute to our culture of empathy and shared success. That’s The Kyndryl Way.
What You Can Expect
Your career with us isn’t just a job—it’s an adventure with purpose. We offer a dynamic, hybrid\-friendly culture that supports your well\-being and empowers you to grow. Our Be Well programs are thoughtfully designed to support your financial, mental, physical, and social health—because we know that when you feel your best, you do your best.
From your very first day, you’ll dive into impactful work that powers the systems our customers rely on every day. You won’t just contribute—you’ll make a difference, tackling meaningful projects that sharpen your skills and fuel your growth.
We’re here to champion your journey. With powerful tools to chart your career path, personalized development goals aligned with your ambitions, and continuous feedback to keep you inspired and on track, you’ll have everything you need to thrive and evolve. You’ll develop in\-demand skills to grow your career and achieve your ambitions with access to cutting\-edge learning opportunities—from certifications with Microsoft, Google, and Amazon to coaching and hands\-on experiences. And through it all, you’ll be part of a culture that values empathy, restless learning, and a devotion to shared success.
We want you to thrive here—and we’re committed to helping you do just that. Ready to make an impact? Join us and help shape what’s next.
Get Referred!
If you know someone that works at Kyndryl, when asked ‘How Did You Hear About Us’ during the application process, select ‘Employee Referral’ and enter your contact's Kyndryl email address.
Salary Context
This $63K-$137K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Kyndryl, 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 $214,900 based on 6,420 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $110,000. This role's midpoint ($100K) sits 53% below the category median. Disclosed range: $63K to $137K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Kyndryl AI Hiring
Kyndryl has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Dallas, TX, US, New York, NY, US. Compensation range: $137K - $279K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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