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About This Role
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Who We Are
Boston Consulting Group partners with leaders in business and society to tackle their most important challenges and capture their greatest opportunities. BCG was the pioneer in business strategy when it was founded in 1963\. Today, we help clients with total transformation\-inspiring complex change, enabling organizations to grow, building competitive advantage, and driving bottom\-line impact.
BCG's Tech and Digital Advantage (TDA) practice focuses on helping clients deliver competitive advantage and business superior performance through data, technology and digital. BCG Platinion sits within the TDA practice and is at the heart of the strategic impact we have with our clients. Our consultants and experts globally work across all industries and provide deep experience and expertise in a wide variety of topics including Tech Advisory and Delivery, Architecture, Enterprise Solutions and Packaged Software, Cybersecurity, and Technology Risk Management.
Our Tech Advisory and Delivery Chapter within BCG Platinion helps clients solve some of their most challenging problems through the development of superior IT concepts and tech solutions. The ideal candidate is both passionate as a consultant and technologist, and can bring their expertise to help develop customized, innovative client solutions.
Tech Advisory \& Delivery Senior AI Tech Consultants at BCG Platinion are:
- Technical experts. They are critical thinkers and have extensive IT expertise that drives innovation for our clients, rooted in practicality.
- Collaborative. They are interdisciplinary team players who seek alignment and establish relationships ranging from cross\-functional stakeholder groups to existing teams.
- Comfortable with ambiguity. They know the path forward isn’t always well\-defined. They are comfortable and confident working through the unknown.
- Change agents. They know how to make change happen across an organization across all levels \- from executives to individual contributors and IT practitioners. They can align and onboard teams to implement new processes and toolsets. They embrace complex challenges and guide an organization to optimize IT best practices.
- Influencers. They build strong relationships to build trust and influence stakeholders. They are able to effectively communicate across Business \& IT stakeholders in order to influence positive change.
- AI and Agentic native. They are versed in the latest AI and Agentic technologies and methodologies and leverage them daily to deliver high impact outcomes and tackle difficult challenges.
What You'll Do
As a Senior AI Tech Consultant, you’ll be given end\-to\-end responsibility for an individual “module” within a BCG client engagement and begin to develop specialized knowledge to help you solve our clients’ problems. You’ll work on a variety of digital topics, applying generalist consulting skills to strategic digital \& technology questions. Working together with clients to understand their issues, you’ll create strategies for change, win buy\-in for your recommendations, and collaborate with fellow BCGers to transform client potential into performance.
Our consultants within the Tech \& Digital Advantage (TDA) Chapter are an integral part of BCG’s core consulting team; we work side\-by\-side with all practice areas to create value and competitive advantage for our clients. You’ll help your clients answer questions such as “How will AI disrupt my business model?”, “How can businesses use data and analytics to create highly personalized outreach delivered through a seamless omnichannel experience?”, “How can I ship higher quality digital services to my customers faster by using agile?” and many more.
You’re Good At:
At Platinion, we expect our consultants to be able to contribute and quickly get up to speed on a variety of topics to support the ever\-changing demands of our clients. However, that broad ability to deliver needs to be grounded in deep technological knowledge in 2\-3 of our primary areas of expertise such as;
Expertise
- Supporting AI and digital transformation efforts by developing strategies around:
+ AI and Agentic Platforms.
+ Data and digital analytics platforms.
+ Cloud infrastructure and technologies.
+ Data management capabilities.
+ Enterprise Tech Platforms.
+ Business IT architecture.
+ Software and product management (including Agile ways of working).
+ Tech, Enterprise Architecture and Data governance.
- Experience with a wide breadth of tech / AI / digital offerings, including but not limited to:
+ Developing roadmaps and current state assessments for IT and AI/Data organizations in large companies across various industries.
+ Orchestrating technical program delivery across business stakeholders, complex vendor landscapes and technology ecosystems.
+ AI / LLM cost optimization and FinOps.
+ IT Benchmarking and recommendations for IT cost take\-out initiatives.
+ Developing future state IT vendor landscape, vendor assessments/selections automation, tech cost benefits analysis, eCommerce, platform valuation and design.
+ Supporting IT functions during mergers and acquisitions, including the functional and technical transformation of an organization’s IT department.
+ Communicating the differences and benefits between multiple cloud computer providers and strategies for migrating from on\-premise architecture.
+ Utilizing working knowledge and experience with GenAI and its potential use cases and client implications to deliver both practical and visionary solutions.
- Experience supporting the design and delivery of AI\-powered products within a functional and/or industry business domain, including:
+ Defining and prioritizing AI product requirements and user stories grounded in domain\-specific workflows.
+ Translating business problems into AI\-enabled product concepts and functional specifications.
+ Supporting agile product delivery including sprint planning, backlog management, and iterative development.
+ Contributing to product testing, quality assurance, and user acceptance activities.
+ Supporting the configuration and integration of AI and generative AI tools into enterprise workflows
+ Supporting definition of product / solution architecture and data integration patterns.
+ Assisting with data readiness activities including mapping, cleansing, and validation across source systems.
+ Contributing to the design of agentic workflows and automation use cases within a functional domain.
+ Supporting vendor evaluations and platform assessments for AI product tooling.
+ Assisting with change management, training, and adoption activities to embed AI products into business operations.
- Optimizing business and IT processes within complex and heavily matrixed organizations and effectively communicating a path forward for clients and internal stakeholders.
- Delivering data and digital platforms using techniques such as Agile, DevSecOps, and Cloud native architectures.
Specific AI product domain expertise topics (preferred):
- FP\&A \& FinOps:
+ Support the design and delivery of AI\-powered products and subject matter depth in one or multiple core finance processes, including FP\&A, financial close and reporting, procure to pay, order to cash, etc., with understanding of underlying finance systems (e.g. ERP, CLM, EPM, etc.) and data (e.g., GL, Orders, Invoices, Contracts, Plan/Forecast, etc.). Experience deploying AI use cases ranging from operational quick wins (e.g., AI chat enabled reporting insights) to strategic transformation (e.g., AI order\-to\-cash automation).
- Supply Chain / S\&OP(E):
+ Support the design and delivery of AI\-powered products and subject matter depth in one or multiple key supply chain processes including demand planning, inventory optimization, procurement, logistics, and fulfillment, with understanding of underlying systems (e.g., ERP, MES, APS) and data (e.g., BOM, Orders, Inventory, etc.). Experience deploying AI use cases ranging from operational quick wins (e.g., stockout detection) to strategic transformation (e.g., Agentic S\&OP transformation).
- Go\-To\-Market (GTM):
+ Support the design and delivery of AI\-powered products and subject matter depth in one or multiple key GTM processes including marketing, sales, customer success, and revenue operations, with understanding of underlying systems (e.g., CRM, CMS, CPQ, SPM) and data (e.g., Customer, Lead/Opportunity, Order/configuration, Territory/Quota, etc.). Experience deploying AI use cases from operational quick wins (e.g., lead scoring, outreach automation) to strategic transformation (e.g., AI\-native revenue operations).
Written communication
- Effectively codifying messaging and data into presentable materials and being comfortable delivering communications output in an efficient manner – accuracy and speed are critical to success of fast\-paced, high\-profile projects.
- Synthesizing wide\-ranging conversations, artifacts and best practices into clear, action\-oriented recommendations.
- Assisting with business development through writing proposals, scoping projects.
- Contributing to our thought leadership through written publications and speaking at events and conferences.
Presentation and Facilitation
- Presenting materials, case updates and escalations to client and internal teams.
- Facilitating working sessions and workshops with both client and internal teams.
- Building relationships with key clients.
What You'll Bring
- Bachelors or Master degree in information technology, computer science, economics, supply chain, logistics or system engineering, business management/administration, or relevant field.
- 2\+ years of practical experience in IT/AI consulting, product management, professional software development, product and program implementation. Hands\-on experience with SQL, Python, or similar. Preferred experience with at least one of the following:
+ AI product management.
+ AI platforms (e.g., AWS Agentcore, Azure Foundry, n8n, Aera, Palantir, Agentforce etc.).
+ Data platforms (e.g., Databricks, Snowflake, etc.).
+ Planning platforms (e.g., Anaplan, Kinaxis, OMP, etc.).
+ SAP / Oracle.
+ Salesforce.
- Exceptional learning and ramp up skills, especially on IT topics such as data and digital platforms .
- Excellent communication and presentation skills.
- Outstanding analytical and conceptual skills.
- Outstanding ability to work creatively, autonomously, analytically, in a fast\-paced problem\-solving environment with a focus on customer and results .
- GenAI tool fluency (e.g., demonstrated use of GenAI tools such as ChatGPT, Claude) and validation of responses.
- Willingness to travel around the globe to work with clients and BCG teams. At times, this role may involve up to 80% travel to client sites and BCG offices. The amount of travel will depend on client needs and nature of projects.
Additional info
What We Offer:
At BCG, we care about our people, and offer best in class benefits to support you personally and professionally including:
- An opportunity to work organically across disciplines and across BCG, we offer a unified and unrivaled opportunity that combines strategic thinking with hands\-on applications.
- A unique experience to work alongside a team of passionate and driven problem\-solvers with a mission to deliver innovative and valuable digital solutions in a supportive environment.
The first year base compensation for this role is $150,000 USD.
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.
- $10 (USD) copays for trips to the doctor, urgent care visits and prescriptions for generic drugs.
- Dental coverage, including up to $5,000 (USD) in orthodontia benefits.
- Vision insurance with coverage for both glasses and contact lenses annually.
- Reimbursement for gym memberships and other fitness activities.
- Fully vested retirement contributions made annually, whether you contribute or not.
- Generous paid time off including vacation, holidays, and annual office closure between Christmas and New Years.
- Paid Parental Leave and other family benefits such as elective egg freezing, surrogacy, and adoption reimbursement.
- Employees, spouses, and children are covered at no cost. Employees share in the cost of domestic partner coverage.
To learn more about our employee benefit please check our Benefits page.
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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 $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.
Boston Consulting Group AI Hiring
Boston Consulting Group has 4 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Summit, NJ, US, Brooklyn, NY, US, New York, NY, 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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