Boston Consulting Group (BCG) is actively hiring for 10 AI and machine learning positions, primarily in AI/ML Engineer (10) roles. Positions are based in Summit, NJ, Brooklyn, NY, London, England, UK. Senior-level roles account for 80% of openings.
Locations
Summit, NJ, Brooklyn, NY, London, England, UK, Kuala Lumpur, Federal Territory of Kuala Lumpur, Malaysia, New York, NY
Open Positions (10)
BCG Platinion | Senior AI Tech Architect - AI Platforms
Inverto | Manager - Senior Manager, Digital, Data & AI
Global Product Director - AI Innovation
BCG Platinion | Lead AI Tech Architect - AI Platforms
BCG Platinion Lead/Principal Architect - Data & AI
BCG Platinion | Lead AI Tech Architect, Public Sector Industry Leader (Defense & Security, Civil Sector)
Editorial Director - AI Campaign
BCG Platinion | Senior AI Tech Architect, Public Sector Industry Leader (Defense & Security, Civil Sector)
BCG Platinion | Principal AI Tech Architect - AI Platforms
What Boston Consulting Group (BCG)'s hiring tells you
With 10 active AI role(s), this company is in the early exploration phase. That can mean either a pilot project being staffed up or a small embedded AI function inside a larger team. Worth investigating directly: ask the recruiter how the AI work is funded and who it reports to. Compensation is not disclosed in postings, which is increasingly out of step with how AI talent expects to be hired.
Questions worth asking in the Boston Consulting Group (BCG) interview loop
The signals above come from public job postings. The signals you actually need come from the conversation. A few questions calibrated to this company's tier:
- Is this AI work funded for at least 18 months, or is it tied to a specific project deadline?
- Will I be the only person doing this, or are there others I will collaborate with day to day?
- What does success look like at six months? At eighteen months?
Boston Consulting Group (BCG) AI and ML Hiring
Boston Consulting Group (BCG) has 10 active AI and ML roles in our dataset. They're focused on AI/ML Engineer hiring. Roles are based in Summit, NJ, Brooklyn, NY, London, England, UK, Kuala Lumpur, Federal Territory of Kuala Lumpur, Malaysia.
Salary Benchmarks
The market median for AI roles is $213,380. AI/ML Engineer roles pay a median of $212,000 across the market. Top-quartile AI compensation starts at $264,148.
Skills in Demand
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.
AI Role Categories
AI/ML Engineer
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.
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.
Market compensation for AI/ML Engineer roles: $212,000 median across 11,866 positions with disclosed pay.
The AI Job Market Today
The AI job market spans 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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.
AI Hiring Overview
The AI job market has 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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.
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.
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.
Frequently Asked Questions
Frequently Asked Questions
Related Resources
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