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About This Role
Job ID: 109991
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Do you want to do work that matters, alongside supportive leaders who will help you grow faster than you ever thought possible? Are you a creative problem\-solver who is energized by challenges? You’ve come to the right place.
YOUR IMPACT
As a Principal Forward Deployed Engineer, you’ll lead the deployment and scaling of a next\-generation AI platform designed to connect strategy to execution through advanced analytics, machine learning, and agentic systems. This role is uniquely field\-facing. You'll work directly with clients, embedded in their environments, navigating real\-world constraints that don't surface in controlled settings. The combination of hands\-on engineering, client\-facing delivery, and direct product influence defines the role.
You will sit at the intersection of software engineering and infrastructure, bringing advanced AI capabilities into real\-world environments and ensuring they work at scale.
You’ll be at the center of how these systems are deployed, adopted, and operated, helping organizations translate complex AI strategies into practical, high\-impact solutions.
You'll take ownership of the platform delivery lifecycle, including the most demanding engagements, where environments are often non\-standard and requirements evolve quickly. You’ll lead large\-scale, multi\-workstream deployments across cloud and hybrid infrastructures in enterprise environments (e.g., AWS, Azure, GCP), guiding architectural decisions and setting direction for complex deployments across engagements.
Your work frequently involves containerized and distributed systems, including Kubernetes\-based environments, where reliability, scalability, and operational stability are critical. From validating performance and resilience to ensuring structured handovers, your work ensures systems are production\-ready and built to last. Along the way, you’ll help shape how delivery is structured across teams, evolving tooling, automation, and delivery practices based on real\-world experience, and working hands\-on to resolve complex issues in production.
The impact of your work is tangible. You’ll enable clients to run advanced AI\-driven workflows and manage intelligent systems within their existing technology ecosystems, often in regulated or multi\-region settings. Working closely with senior technical stakeholders, you’ll help define deployment strategies, navigate complex challenges, and guide adoption through practical, experience\-led best practices. Your perspective from the field will also feed directly into product development, influencing how QuantumBlack, AI by McKinsey’s technology continues to evolve. You'll maintain a continuous feedback loop with the product team — surfacing patterns from the field, contributing to issue resolution, and helping ensure that real\-world deployment experience shapes the platform's evolution.
Based in one of our North America offices, you’ll work closely with engineers, product teams, and technical experts across QuantumBlack, AI by McKinsey’s global community. Beyond leading deployments, you’ll collaborate with data scientists, machine learning engineers, designers, and technologists on interdisciplinary initiatives, contributing to a broader ecosystem of AI innovation. You’ll also play a key role in developing others—mentoring engineers, reviewing approaches, and helping teams raise the bar for quality and delivery. Together, you’ll help operationalize advanced AI systems across industries, driving successful delivery, validation, and adoption in client environments.
At QuantumBlack, AI by McKinsey, you’ll thrive in an unparalleled environment for growth. You’ll develop a sought\-after perspective by connecting technology and business value, work across industries, and collaborate with multidisciplinary teams to unlock the transformative potential of AI, while advancing as a technologist and leader.
YOUR GROWTH
Driving lasting impact and building long\-term capabilities with our clients is not easy work. You are the kind of person who thrives in a high performance/high reward culture \- doing hard things, picking yourself up when you stumble, and having the resilience to try another way forward.
In return for your drive, determination, and curiosity, we'll provide the resources, mentorship, and opportunities you need to become a stronger leader faster than you ever thought possible. Your colleagues—at all levels—will invest deeply in your development, just as much as they invest in delivering exceptional results for clients. Every day, you'll receive apprenticeship, coaching, and exposure that will accelerate your growth in ways you won’t find anywhere else.
When you join us, you will have:
- Continuous learning: Our learning and apprenticeship culture, backed by structured programs, is all about helping you grow while creating an environment where feedback is clear, actionable, and focused on your development. The real magic happens when you take the input from others to heart and embrace the fast\-paced learning experience, owning your journey.
- A voice that matters: From day one, we value your ideas and contributions. You’ll make a tangible impact by offering innovative ideas and practical solutions, all while upholding our unwavering commitment to ethics and integrity. We not only encourage diverse perspectives, but they are critical in driving us toward the best possible outcomes.
- Global community: With colleagues across 65\+ countries and over 100 different nationalities, our firm’s diversity fuels creativity and helps us come up with the best solutions for our clients. Plus, you’ll have the opportunity to learn from exceptional colleagues with diverse backgrounds and experiences.
- World\-class benefits: On top of a competitive salary (based on your location, experience, and skills), we provide a comprehensive benefits package to enable holistic well\-being for you and your family.
YOUR QUALIFICATIONS AND SKILLS
Bachelor's, Master's in computer science, machine learning, applied statistics, mathematics, engineering, artificial intelligence, or a related field
8\+ years of hands\-on experience in software, platform, or infrastructure engineering, with a track record of leading enterprise\-scale platform rollouts
Strong full\-stack engineering — proficiency in Python and modern web frameworks (React, NextJS or equivalent)
Experience designing, deploying, and managing cloud\-based systems (AWS, Azure, or GCP), including containerization (Docker) and orchestration frameworks, with hands\-on experience operating and troubleshooting production systems; deep expertise in Kubernetes cluster architecture, installation, configuration, and lifecycle management at production scale
Experience leading complex deployments, guiding architectural decisions, and driving delivery standards across engagements in multi\-stakeholder environments
Strong experience with CI/CD pipelines and Infrastructure as Code (e.g., GitHub Actions, GitLab CI, Terraform, Ansible, Helm), contributing to scalable delivery automation
Strong understanding of data architectures and platform design, including hands\-on experience with relational databases (e.g., PostgreSQL) and familiarity with graph databases (e.g., Neo4j), alongside data pipelines and system integration patterns
Experience with AI\-native platform concepts, including model integration patterns, agentic architectures (tool calling, prompt orchestration, multi\-agent workflows), and data pipelines that support AI\-driven applications is a plus
Strong problem\-solving skills with a structured approach to debugging and resolving issues in complex, non\-standard environments, including operating and troubleshooting distributed systems in production
Experience with DevSecOps, infrastructure security, and networking fundamentals (e.g., IAM/SSO, RBAC, secrets management, VPNs, DNS, load balancing); experience with delivery standards, runbook development, and automation frameworks is a strong advantage
Familiarity with observability, monitoring, and compliance practices, and experience working in secure or regulated environments is preferred
Willingness to travel
Ability to communicate effectively in client\-facing settings, including leading technical discussions, facilitating workshops, and presenting to senior stakeholders
Please review the additional requirements regarding essential job functions of McKinsey colleagues.
Our unwavering commitment to integrity drives everything we do, guiding us to always act in the best interests of our clients, our people, and the communities we serve.
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 McKinsey & Company, 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. Senior-level AI roles across all categories have a median of $230,000.
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.
McKinsey & Company AI Hiring
McKinsey & Company has 4 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Positions span San Jose, CA, US, Boston, MA, US, Atlanta, GA, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 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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