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About This Role
Who We Are
At Kyndryl, we design, build, manage and modernize the mission\-critical technology systems that the world depends on every day. So why work at Kyndryl? We are always moving forward – always pushing ourselves to go further in our efforts to build a more equitable, inclusive world for our employees, our customers and our communities.
The Role
This strategic role is responsible for architecting differentiated Application Managed Services (AMS) solutions for Kyndryl’s strategic enterprise pursuits. The Solution Architect combines deep application services expertise with agentic AI, policy as code, automation, observability, DevSecOps, and modern engineering practices to create autonomous operating models that improve client outcomes, reduce cost, and accelerate innovation.
Primary Responsibilities
- Lead end\-to\-end solutioning for complex AMS opportunities from qualification through contract signature.
- Design AI\-enabled operating models spanning L.15/L2/L3 support, application engineering, SRE, DevSecOps, platform engineering, and continuous modernization.
- Architect autonomous application operations using agentic AI, policy as code, intelligent automation, and observability.
- Lead executive discovery workshops and translate business objectives into compelling technical and commercial solutions.
- Develop transition, transformation, governance, staffing, sourcing, and pricing strategies.
- Own the technical solution for RFPs, RFIs, proposals, and client presentations.
- Present complex AMS solutions to clients with conviction and credibility.
- Partner with Sales, Strategic Deals, Delivery, Consulting, Cloud, Security, Finance, and AI teams.
- Contribute reusable assets, reference architectures, and thought leadership.
Agentic AI Leadership
- Design human\-on\-the\-loop operating models where AI agents execute routine operational work under governance.
- Apply agentic AI across incident management, problem management, change management, knowledge management, testing, code remediation, release validation, and application optimization.
- Embed AI governance, policy as code, security, compliance, and responsible AI practices into every solution.
- Drive continuous expansion of autonomous capabilities throughout the application lifecycle.
Kyndryl currently does not require employees to be fully vaccinated against COVID\-19, however, if you are hired to work at a client, customer, or partner location, you may be required to show proof of vaccination to align with their respective COVID\-19 vaccination policies. Those who believe they are eligible may apply for a medical or religious accommodation prior to the start of employment.
Who You Are
You’re good at what you do and possess the required experience to prove it. However, equally as important – you have a growth mindset; keen to drive your own personal and professional development. You are customer\-focused – someone who prioritizes customer success in their work. And finally, you’re open and borderless – naturally inclusive in how you work with others.
Required Qualifications
- 10\+ years of enterprise Application Managed Services experience.
- Proven experience architecting multi\-million\-dollar outsourcing or managed services pursuits.
- Deep knowledge of SDLC, ITIL, DevSecOps , SRE, cloud\-native architectures, application modernization, and enterprise application landscapes.
- Executive communication, facilitation, negotiation, and consultative selling skills.
Preferred Qualifications
- Experience with agentic AI, AIOps, observability, intelligent automation, LLMs, RAG, AI orchestration, and policy as code.
- Healthcare, insurance, financial services, manufacturing, retail, or public sector experience.
- Experience leading global delivery organizations and large pursuit teams.
Core Competencies
- Strategic Solution Design
- Executive Presence
- Commercial Acumen
- Client Consulting
- Application Architecture
- AI \& Automation Strategy
- Cross\-functional Leadership
- Innovation
- Influencing Without Authority
Education
- Bachelor's degree in Computer Science , Engineering, Information Systems, or related field .
- Master's degree preferred.
Travel
- Willingness to travel up to 30–50% based on client and pursuit needs.
Success Measures
- Growth in AMS signings and qualified pipeline .
- Improved pursuit win rates.
- Adoption of autonomous and AI\-enabled operating models.
- Creation of reusable intellectual property and solution assets.
- Positive client feedback and executive sponsorship.
- Commercially viable , executable, and differentiated solutions.
Leadership Expectations
- Model Kyndryl's values of empathy, innovation, trust, and customer obsession.
- Lead through influence across matrixed organizations.
- Champion modern application services and autonomous operations.
- Continuously improve Kyndryl's AMS offerings and go \-to\-market approach.
The compensation range for the position in the U.S. is \- $179,760 to $341,520 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): $215,640 to $409,800
California (All Other): $197,640 to $375,600
Colorado: $179,760 to $341,520
Massachusetts: $179,760 to $375,600
New York City: $215,640 to $409,800
Washington: $197,640 to $375,600
Washington DC: $197,640 to $375,600
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
Diversity is a whole lot more than what we look like or where we come from, it’s how we think and who we are. We welcome people of all cultures, backgrounds, and experiences. But we’re not doing it single\-handily: Our Kyndryl Inclusion Networks are only one of many ways we create a workplace where all Kyndryls can find and provide support and advice. This dedication to welcoming everyone into our company means that Kyndryl gives you – and everyone next to you – the ability to bring your whole self to work, individually and collectively, and support the activation of our equitable culture. That’s the Kyndryl Way.
What You Can Expect
With state\-of\-the\-art resources and Fortune 100 clients, every day is an opportunity to innovate, build new capabilities, new relationships, new processes, and new value. Kyndryl cares about your well\-being and prides itself on offering benefits that give you choice, reflect the diversity of our employees and support you and your family through the moments that matter – wherever you are in your life journey. Our employee learning programs give you access to the best learning in the industry to receive certifications, including Microsoft, Google, Amazon, Skillsoft, and many more. Through our company\-wide volunteering and giving platform, you can donate, start fundraisers, volunteer, and search over 2 million non\-profit organizations. At Kyndryl, we invest heavily in you, we want you to succeed so that together, we will all succeed.
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 $179K-$409K range is above the 75th percentile 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 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 $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 ($294K) sits 35% above the category median. Disclosed range: $179K to $409K.
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.
Kyndryl AI Hiring
Kyndryl has 3 open AI roles right now. They're hiring across AI/ML Engineer. Positions span US, New York, NY, US. Compensation range: $348K - $409K.
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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