Interested in this AI/ML Engineer role at BOARD International?
Apply Now →Skills & Technologies
About This Role
At Board, we help enterprises plan smarter, drive outcomes and lead transformation with one single Intelligent Planning Platform. Trusted by thousands of leading organizations, over the last 28 years, we have designed our Product with enterprise planning agility in mind and the passionate care of our people as our main driver.
We strongly believe every colleague brings unique value to our whole organization. We collaborate openly and effectively to deliver results and celebrate our shared success, thrive on innovation and embrace a growth mindset to aim higher every day. If you’re a forward\-thinker looking for the great next step in your career within an innovative and collaborative environment, Board is the right workplace for you!
We are looking for an Associate AI Engineer to join our AI Center of Excellence on a 6\-month contract. In this role, you will work alongside AI engineers and business stakeholders to design, build, and deploy AI\-powered solutions that improve business processes and drive operational efficiency across the organization.
You will contribute to real\-world AI initiatives, leveraging technologies such as Large Language Models (LLMs), Retrieval\-Augmented Generation (RAG), and agent\-based architectures to address business challenges and deliver measurable impact.
What you’ll do* Partner with teams across Customer Success, Support, Professional Services, Sales, and other business functions to identify and deliver high\-impact AI use cases that improve productivity and operational efficiency.
- Understand business objectives and technical requirements, and design AI\-powered solutions to address real\-world challenges.
- Build, test, and deploy AI applications and production\-ready prototypes using state of the art technologies.
- Design and implement AI\-enabled workflows, including intelligent document processing, multi\-agent solutions, AI\-based routing, and decision\-support capabilities.
- Support the end\-to\-end delivery of AI initiatives, from use case definition and solution design through deployment and adoption.
- Integrate AI solutions with enterprise platforms and business systems, including CRM, ERP, ITSM, marketing, finance, and procurement applications.
- Contribute to the development and maintenance of reusable AI components, tools, and internal best practices.
- Collaborate with AI engineers and stakeholders in architecture reviews, testing, and knowledge\-sharing activities.
- Ensure AI solutions align with governance, security, compliance, and responsible AI standards, including monitoring for quality, reliability, and potential bias.
What We’re Looking For* Academic background in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Software Engineering, or a related field.
- Knowledge of AI and GenAI fundamentals, either through academic or professional work.
- Experience developing production\-level solutions using Python and working with APIs, data pipelines, or AI frameworks.
- Experience working with Large Language Models (LLMs), Retrieval\-Augmented Generation (RAG), vector databases, and modern AI development tools.
- Knowledge of Azure AI Services or other cloud\-based AI platforms is a plus.
- Understanding of software development best practices and the AI solution lifecycle.
- Strong analytical and problem\-solving skills, with the ability to translate business challenges into practical solutions.
- Excellent communication skills, including the ability to explain technical concepts to both technical and non\-technical audiences.
- Collaborative mindset with the ability to build effective working relationships across teams.
- Customer\-focused approach and genuine interest in improving business outcomes through technology.
- Curious, proactive, and eager to learn new technologies in a fast\-paced environment.
- Adaptable and comfortable working with ambiguity, changing priorities, and evolving business requirements.
- Strong organizational and time\-management skills, with attention to detail and a commitment to delivering high\-quality work.
- Creative thinker with a passion for innovation, continuous improvement, and emerging AI technologies.
Our Commitment to Diversity, Equity and Inclusion
Join a company that believes in the added value of diversity, inclusion, and belonging. We foster a working environment in which all people are respected and valued, for all aspects which make them unique. We hire you for who you are, and we want you to bring your true self to work every day! Board International is an equal opportunity employer and is committed to a diverse and inclusive workforce.
*Your personal data will be stored for as long as it is necessary to process the job applications that you submitted and for the provision of the service that you requested. Your personal data may also be processed for the fulfillment of the obligations provided for by law. Your data will in any case be deleted without unjustified delay once the aforementioned legal obligations have been fulfilled. Your personal data are collected and used by Board International SA and/or its subsidiaries that are located in the EU or outside on the basis of the appropriate safeguards provided by the European Regulation 2016/679\. At any time you may request to access, to correct and/or delete your personal data used by Board International SA or by its subsidiaries for recruiting purposes.*
For further question, please refer to our Privacy Policy at https://www.board.com/en/privacy\-policy
LeNTy0zfun
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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At BOARD International, 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 $181,170 based on 12,692 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $97,880.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
BOARD International AI Hiring
BOARD International has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Boston, MA, US.
Location Context
AI roles in Boston pay a median of $215,350 across 442 tracked positions. That's 8% above 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.