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About This Role
Overview
Location: Boston or New York City Practice: SPACE (Digital and Artificial Intelligence)
About the Role
Arthur D. Little (ADL) is seeking a Consultant - Forward Deployed AI Engineer to join our SPACE team. This is a highly hands-on role for an artificial intelligence practitioner who combines strong engineering skills with the ability to work directly with clients.
The role is about building: rapidly developing prototypes to test ideas and demonstrate value, then turning successful prototypes into robust solutions that operate at enterprise scale. You should be equally comfortable writing code, working through an ambiguous client problem, and explaining your solution to technical and business stakeholders.
Why ADL
At Arthur D. Little, you will work with a global network of industry experts, data scientists, engineers, and strategists driving digital transformation across sectors. The SPACE team is at the forefront of shaping how artificial intelligence and data redefine value creation, combining ADL's strategic heritage with deep technical expertise and hands-on delivery.
This role offers the opportunity to help clients move beyond artificial intelligence strategy and experimentation to build solutions that deliver measurable business impact, while developing teams, shaping client relationships, and contributing to the growth of ADL's artificial intelligence capabilities in the Americas. Responsibilities
What You Will Do
- Work directly with clients to translate business problems into technical solutions.
- Rapidly prototype artificial intelligence and machine learning applications to test feasibility and business value.
- Take successful prototypes into production, applying sound software engineering practices for scalability, reliability, security, and maintainability.
- Design, build, test, and deploy solutions using generative artificial intelligence, artificial intelligence agents, retrieval-augmented generation, machine learning, and other appropriate approaches.
Integrate artificial intelligence solutions with enterprise data, applications, workflows, and existing technology environments.
- Evaluate models, architectures, and technologies based on performance, cost, reliability, and business requirements.
- Work side-by-side with ADL colleagues, client technical teams, and external technology partners.
- Communicate technical approaches, trade-offs, results, and recommendations clearly to both technical and business audiences.
Qualifications
What We Are Looking For
- Experience: Typically 2-5 years of professional experience in artificial intelligence, machine learning, software engineering, data science, or related technical roles, with demonstrated hands-on experience building and deploying artificial intelligence solutions. Experience in consulting, professional services, or other client-facing environments is a plus.
- A hands-on builder: Strong programming skills and demonstrated experience personally developing artificial intelligence or machine learning solutions.
- Prototype-to-production capability: Able to move quickly from an idea to a working prototype - and then engineer it into a scalable production solution.
- Modern artificial intelligence expertise: Practical experience with large language models, artificial intelligence agents, retrieval-augmented generation, model evaluation, machine learning, and related technologies.
- Strong software engineering fundamentals: Experience with Python, application programming interfaces, system integration, testing, version control, cloud environments, deployment, and monitoring.
- Client orientation: Comfortable working directly with clients, asking the right questions, navigating ambiguity, and explaining complex technical topics clearly.
- Pragmatism and curiosity: Able to learn new technologies quickly and select the right approach for the problem rather than defaulting to a particular tool or framework.
Relevant technologies may include PyTorch, Hugging Face, LangChain, Databricks, Microsoft Azure, Amazon Web Services, Google Cloud, vector databases, or similar platforms. Specific technologies are less important than demonstrated ability to learn and build.
Arthur D. Little is an equal-opportunity employer. We are committed to ensuring that no employee, contractor, vendor, or job applicant is discriminated against because of their race, color, nationality, ethnic or national origin, religion or belief, sex, marital or civil partner status, veteran status, sexual orientation, age, gender reassignment, or disability.
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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Arthur D. Little, 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 $212,000 based on 11,866 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 $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Arthur D. Little AI Hiring
Arthur D. Little has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Boston, MA, US.
Location Context
AI roles in Boston pay a median of $215,000 across 323 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 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.
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.
Frequently Asked Questions
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