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About This Role
Who We Are
BCG X is the tech build & design unit of BCG. We bring together advanced tech knowledge and ambitious entrepreneurship to help CEOs and senior world leaders launch their next big bets and multiply our collective impact. With nearly 3,000 technologists, scientists, programmers, engineers, and human-centered designers located across 80+ cities, BCG X builds and designs platforms and software to address the world’s most important challenges and opportunities.
Through a critical mass of multidisciplinary expertise, access to the world’s leading technologists, and the power of practical collaboration, BCG X enables end-to-end partnerships to rapidly invent and build new solutions that are ready to run from day one. Together, we’re creating the bold and disruptive products, services, and businesses of tomorrow.
We Are BCG X
We’re a diverse team of more than 3,000 tech experts united by a drive to make a difference. Working across industries and disciplines, we combine our experience and expertise to tackle the biggest challenges faced by society today. We go beyond what was once thought possible, creating new and innovative solutions to the world’s most complex problems. Leveraging BCG’s global network and partnerships with leading organizations, BCG X provides a stable ecosystem for talent to build game-changing businesses, products, and services from the ground up, all while growing their career. Together, we strive to create solutions that will positively impact the lives of millions.
What You'll Do
We are seeking exceptional candidates with experience and passion to fill Delivery AI Product Analyst in BCG’s new business unit, BCG X. This global business unit brings together cutting-edge AI, visionary business ventures, and unique software and products in the most strategic industry applications to deliver superior value and sustained impact. All of BCG’s core product capabilities are housed within the business unit including product management, design, development, support, and data analytics experts who work in deep collaboration with BCG practice areas, consulting teams, and clients. This team, of highly motivated and insightful colleagues, will provide an opportunity to work in an entrepreneurial environment supported by a global management consulting firm, which is the world’s leading advisor on business strategy.
A BCG Delivery AI Product Analyst is someone who will work with product manager and senior leadership in various business groups to help drive capabilities and support increased commercialization of BCG proprietary data and software assets. This person will work together with technical and commercial teams from the initial business case to roadmap delivery, launch, and ongoing support operations. We want someone with an entrepreneurial drive and demonstrated experience leading enterprise or large-scale product efforts across product strategy, marketing, finance, engineering, support, and executive stakeholders.
Additionally, you will work in a growing global team, providing industry & topic expertise and insights whilst working together with case and proposal teams to provide knowledge assets, methodologies, software solutions, analysis and expert advisory. You’ll serve as a thought partner and content expert to case teams and topic/sector leaders, helping structure and solve complex issues This is an exciting opportunity to jump into a unique position at a world class firm.
What You'll Bring
Product Management
- Co-own the product roadmap with the Product Manager and keep it tied to business goals and user needs
- Conduct user research and usability tests, and use the results alongside market analysis and client feedback to improve the product
- Gather and prioritize requirements in stakeholder workshops, then write them up as user stories with acceptance criteria
- Build user personas and use AI design tools to prototype and iterate with the UI/UX team
- Run Scrum ceremonies with the engineering team and clear blockers to keep features shipping on time
- Join technical discussions and translate between engineering and business stakeholders so product decisions stay feasible
- Keep feature lists, user guides and release notes current, and update stakeholders on progress and milestone
Product Delivery
- As a thought partner to case teams and clients, define the business problem early and shape a solution around it
- Apply BCG Transformation and Post-Merger-Integration (PMI) methodology on cases and configure the product for each client’s program
- Manage MVP-based implementation plans as the client’s day-to-day point of contact, and set realistic expectations about what the product covers today
- Lead demos and training sessions that get case teams and client users working in the product
- Triage client issues and work them through to resolution with Engineering and Support
- Convert case learnings into proprietary assets and input for the roadmap
AI-Native Ways of Working
- Set up and maintain the team’s AI harness, including a product knowledge base that captures context, personas, business rules and past decisions, plus connectors to Azure DevOps, Confluence and SharePoint
- Create a library of reusable skills, such as a story writer, BRD generator and release notes generator, and update/maintain them as the product changes
- Convert routine product owner work (backlog refinement, story writing and edge-case testing, sprint review prep, stakeholder updates) into repeatable AI workflows with defined inputs and a standard output format
- Help design agentic workflows that chain AI tools and models to automate repetitive, multi-step tasks
- Coach clients and case teams on building AI dashboards and structuring outputs data through prompts
- Test new AI tools, models and agentic techniques against client use cases and recommend which ones to adopt
- Make sure every product decision and AI-generated output has a named human owner, and protect client data under BCG’s AI usage and responsible AI guidelines
YOU BRING (SKILLS, EXPERIENCE & QUALIFICATIONS)
Technco-Functional Proficiency and Collaboration
- 2-3 years of consulting experience (preferred) or 3-4 years in industry, ideally in a business analysis or product role on a SaaS product
- Exposure to transformation, restructuring, change management or PMI, with an appetite to build depth in these topics
- Solid grasp of P&L and balance sheet KPIs plus proficiency in PowerPoint and financial modelling in Excel
- A good understanding of the software development lifecycle and Agile delivery (no coding required); Tableau or Alteryx skills are an advantage
- Hands-on experience with LLMs and AI tools such as ChatGPT, Claude or Copilot, including prompt engineering, skill building, and agentic workflow design
- Structured problem solving and analytical rigor, with the planning discipline and attention to detail to follow through on commitments
- Experience communicating with and influencing senior stakeholders across global teams, and the judgment to know when to escalate
- Entrepreneurial drive and a record of hitting stretch goals in fast-moving environments
- Comfortable working independently or in distributed teams, and quick to pick up new tools and methods
Who You'll Work With
- Client, Case, & BCG Vantage team members
- Product Engineering, Product Delivery, and Infrastructure Support team
- Helpdesk, Reporting & Data Analytics team
- Topic/Product/Case Leadership
Additional info
The first-year base compensation for this role is $99,000.
In addition to your base salary, you will also be eligible for an annual discretionary performance bonus and BCG's Profit Sharing and Retirement Fund (PSRF) contribution. BCG also provides a market leading benefits package described below. At BCG, we are committed to offering a comprehensive benefit program that includes everything our employees and their families need to be well and live life to the fullest. We pay the full cost of medical, dental, and vision coverage for employees - and their eligible family members. That's zero dollars in premiums taken from employee paychecks. All our plans provide best in class coverage:
- Zero dollar ($0) health insurance premiums for BCG employees, spouses, and children
- Low $10 (USD) copays for trips to the doctor, urgent care visits and prescriptions for generic drugs
- Dental coverage, including up to $5,000 in orthodontia benefits
- Vision insurance with coverage for both glasses and contact lenses annually
- Reimbursement for gym memberships and other fitness activities
- Fully vested Profit Sharing Retirement Fund contributions made annually, whether you contribute or not, plus the option for employees to make personal contributions to a 401(k) plan
- Paid Parental Leave and other family benefits such as elective egg freezing, surrogacy, and adoption reimbursement
- Generous paid time off including 12 holidays per year, an annual office closure between Christmas and New Years, and 15 vacation days per year (earned at 1.25 days per month)
- Paid sick time on an as needed basis
- Employees, spouses, and children are covered at no cost. Employees share in the cost of domestic partner coverage.
#BCGXJob
Boston Consulting Group is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, age, religion, sex, sexual orientation, gender identity / expression, national origin, disability, protected veteran status, or any other characteristic protected under national, provincial, or local law, where applicable, and those with criminal histories will be considered in a manner consistent with applicable state and local laws.
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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Boston Consulting Group, 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Boston Consulting Group AI Hiring
Boston Consulting Group has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Chicago, IL, US.
Location Context
AI roles in Chicago pay a median of $193,440 across 429 tracked positions. That's 9% below the national 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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