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About This Role
What Makes Us a Great Place to Work
We are proud to be consistently recognized as one of the world’s best places to work. We are currently the top ranked consulting firm on Glassdoor’s Best Places to Work list and have earned the #1 overall spot a record seven times. Extraordinary teams are at the heart of our business strategy, but these don’t happen by chance. They require intentional focus on bringing together a broad set of backgrounds, cultures, experiences, perspectives, and skills in a supportive and inclusive work environment. We hire people with exceptional talent and create an environment in which every individual can thrive professionally and personally.
Who You’ll Work With
You will be part of a team building Bain’s integrated suite of agentic decisioning tools that gives large organizations always-on intelligence across their most important growth decisions. The role sits at an intersection few organizations can offer: the analytical depth and client access of a top-tier consultancy, the technical ambition of a product company, and engineering work at the frontier of applied AI.
You will work inside Bain’s standing product team, building software that helps the world’s largest companies answer the questions that drive their business: where to invest, where to focus, and how to win. The impact of what you build is direct and immediate, and the platform compounds in value with every release.
The platform has two layers working together: an agentic core and a set of decision modules, each solving a distinct commercial problem. The build lifecycle is connected to the real world: as case teams work alongside clients across industries and geographies, that feedback flows straight back to engineering and into the product. Every client engagement makes the platform smarter, broader, and more resilient.
This is a startup inside an established company. You get genuine early-stage ownership and real influence over architecture and product direction, backed by Bain’s global scale, client relationships, and leadership commitment; the pace of a startup with the runway of a global firm.
We are looking for engineers who see how to make a product better and bring that instinct to every sprint without being asked, and who are comfortable with problems that do not have a known answer. The work involves real-time decision engines, cloud-native infrastructure, and data problems with no off-the-shelf answer. Experience with agentic systems matters: tool and context protocols such as MCP, evaluation harnesses, memory design, and entity resolution across messy enterprise data. This is applied AI in production, at scale, with real users depending on what you build. The field is moving quickly and the team is built to move with it, so the willingness to keep learning counts for as much as what you already know.
Where You’ll Fit Within the Team
The AI Engineer is a core builder of the Growth AI Performance System platform’s Agentic Core and Interface: the shared foundation that all three product modules are built on. This is not a module-level role; you are building the central intelligence layer: the agentic workflows, retrieval and knowledge pipelines, tool integrations, and interface infrastructure that every product in the suite depends on. Moving solutions from early POC and MVP through to scaled production deployments, you are responsible for the reliability, quality, and coherence of the systems that real client teams use every day.
You will work across the platform and product modules focused on marketing ROI, brand, and pricing strategy, translating the analytical and commercial questions that client teams are trying to answer into reliable, production-grade agentic systems that deliver those answers at scale.
The role requires both deep technical capability and genuine client awareness. You will engage directly with consulting teams and, at times, with senior stakeholders, presenting how the Agentic Core works, translating system design decisions into business terms, and using that feedback to make the platform better. The build loop here is tight: client engagement informs engineering, and every release makes the suite more capable.
This role sits at the intersection of research and production engineering. The problems are rarely solved by off-the-shelf tooling: they require judgment about when agentic approaches add real value, how to build systems that fail gracefully, and how to measure quality in ways that matter to real users on real client engagements.
What You’ll Do
Building the Agentic Core
- Build and own core components of the Growth AI Performance System Agentic Core, the shared intelligence layer that all product modules depend on, including multi-agent orchestration frameworks, tool registries, shared memory and state management, and the interface infrastructure that surfaces platform capabilities to users
- Build AI applications for enterprise use cases across the full delivery lifecycle, from rapid POCs and MVPs through to scaled production deployments, designing AI workflows that execute multi-step tasks reliably and safely - building evals to measure output quality, benchmark model performance, and validate reliability before and after deployment
- Implement agentic patterns with strong judgment about when they add real value; build robust agent capabilities including context engineering, memory and state management, reasoning and planning, reflection, orchestration, routing, tool selection, function calling, and tool integration, with clear separation of concerns so the tool ecosystem scales cleanly
- Design and build advanced retrieval and knowledge pipelines across diverse data structures (vector, keyword, hybrid, and graph-based), covering embedding generation and optimization, indexing strategies, metadata design, relevance tuning, caching, access controls, and source attribution
- Apply fine-tuning and parameter-efficient tuning techniques where they measurably outperform prompting and retrieval alone; run model experimentation and early-stage research to inform architecture decisions
- Integrate platform capabilities into enterprise environments, balancing quality, latency, cost, privacy, and adoption; build reusable components and accelerators that scale across all three product modules
Engineering quality and operations
- Engineer for real delivery across the full software development lifecycle: write clean, testable, maintainable code; build APIs and microservices for cloud-native deployment; implement MLOps, AIOps, and GenAIOps practices including CI/CD, reproducibility, environment parity, and versioning for models, prompts, and agents
- Build evaluation and observability for the Agentic Core: tracing and instrumentation of agentic execution, regression test suites, automated scoring pipelines, and iteration loops for prompt and policy optimization
- Design evaluation frameworks that track hallucination rate and factual consistency, relevance and retrieval precision/recall, latency and throughput, and cost per task; feed user signals and human feedback into continual improvement cycles and ongoing model updates
- Apply data science and ML skills as needed to materially improve platform performance or reliability: data preparation, feature engineering, model selection, training, validation, and reproducible evaluation pipelines
- Design for secure enterprise deployment: access controls, auditability, responsible handling of sensitive data, and responsible AI guardrails, including fallbacks, red-teaming, and human-in-the-loop (HITL) review; implement security, privacy, and governance requirements across all platform services; partner with Bain’s global Responsible AI and ethics teams to keep the platform aligned with firm standards
Internal stakeholder communication skills
- Engage directly with consulting teams and senior stakeholders to understand how the Agentic Core is being used, where it falls short, and what needs to change; translate that feedback into concrete engineering improvements
- Present complex agentic system design, tradeoffs, and implementation decisions to senior stakeholders, including C-suite and executive leadership, in clear, non-technical terms that build confidence in the platform
- Contribute to discovery sessions and solution shaping with case teams; operate as a credible, composed technical voice that connects engineering decisions to commercial outcomes; act as a thought partner on AI strategy, architecture choices, emerging capabilities, and implementation roadmaps
- Drive innovation through out-of-the-box thinking; participate in POCs and innovation workstreams to validate new tools and technologies; stay current with emerging AI and agentic trends and share learnings across the team
- Partner closely with product, engineering, data science, ethics, and infrastructure colleagues to build robust, compliant AI systems
- Mentor and upskill engineers and case team members on best practices in RAG, agents, prompt engineering, and AI safety
About You
Technical
- Strong proficiency in Python and experience building and integrating production services; hands-on experience with LLM APIs, agentic orchestration frameworks, and deploying services on Azure or AWS
- Experience designing and implementing agentic workflows and multi-step LLM pipelines; deep familiarity with context engineering, memory and state management, tool integration, routing logic, planning and reflection, and orchestration patterns; working knowledge of ReAct, RLAIF, and other HITL and feedback-loop techniques
- Experience building retrieval and knowledge pipelines across vector, keyword, hybrid, and graph-based data structures, including hands-on use of vector and graph databases; understanding of embedding optimization, indexing strategies, relevance tuning, and how retrieval connects to LLM-powered workflows
- Proficiency with MLOps and AIOps practices: CI/CD for AI services, versioning for models, prompts, and agents, automated evaluation pipelines, and observability for agentic systems
- Advanced prompt engineering, context engineering, and conversation design; strong grounding in evaluation design, experimentation frameworks, and data labeling strategies for LLM applications
- Solid system design and architecture skills, with deep familiarity with cost optimization and computational tradeoffs for LLM workloads
- Ability to apply data science and ML skills to improve system performance: feature engineering, model selection, training, validation, and reproducible evaluation pipelines
- Strong engineering fundamentals: clean, testable, maintainable code; code review; version control; automated testing; and performance profiling
- Native AI fluency in day-to-day engineering, active use of AI-assisted coding tools (Cursor, Claude Code, or equivalent) to accelerate development and prototype faster
- Demonstrated commitment to responsible AI practices: access controls, auditability, bias awareness, and the judgment to know when a system is not ready to deploy
Experience
- 10 or more years in software engineering or applied AI roles with significant hands-on building responsibilities
- Track record of shipping production AI or ML systems end-to-end, from early experimentation through to live deployment
- Experience contributing to complex, multi-stack generative AI programs from conception through production, including mentoring other engineers along the way
- Experience working in agile, cross-functional product or consulting-adjacent environments; strong troubleshooting and issue resolution skills; comfortable operating in high-ambiguity environments
- Direct experience engaging with clients or senior business stakeholders in a technical capacity, presenting, advising, or shaping solutions in a consulting-adjacent environments is a strong advantage
Education
Associate’s degree / Bachelor’s degree in computer science, engineering, mathematics, or a related technical field; or an equivalent combination of education, training, and experience
U.S. Compensation Information
Compensation for this role includes base salary, annual discretionary performance bonus, 401(k) plan with an annual employer contribution based on years of service and Bain’s best in class benefits package (details listed below).
Some local governments in the United States require a good-faith, reasonable salary range to be included in job postings for open roles. The estimated annualized compensation for this role is as follows:
- In Massachusetts, the good-faith, reasonable annualized full-time salary range for this role is between $235,750 - $282,500; placement within this range will vary based on several factors including, but not limited to experience, education, licensure/certifications, training and skill level
- In Illinois, the good-faith, reasonable annualized full-time salary range for this role is between $225,750 - $270,500; placement within this range will vary based on several factors including, but not limited to experience, education, licensure/certifications, training and skill level
- Annual discretionary performance bonus
- This role may also be eligible for other elements of discretionary compensation
- 4.5% 401(k) company contribution, which increases after 3 years of service and is 100% vested upon start date
Bain & Company’s comprehensive benefits and wellness program is designed to help employees achieve personal independence, protection and stability in the areas most important to you and your family.
- Bain pays 100% individual employee premiums for medical, dental and vision programs, offering one of the most comprehensive medical plans for employees without impacting your paycheck
- Generous paid time off, including parental leave, sick leave and paid holidays
- Fully vested 401(k) company contribution
- Paid Life and Long-Term Disability insurance
For all other locations, the good-faith, reasonable annualized full-time salary range for this role is commensurate with competitive geographic market rates for this role and will vary based on several factors including, but not limited to experience, education, licensure/certifications, training and skill level.
It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.
Salary Context
This $235K-$270K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $178K across 2239 roles with salary data).
View full AI/ML Engineer salary data →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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Bain & Company, 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 $212,040 based on 13,780 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,250. This role's midpoint ($253K) sits 19% above the category median. Disclosed range: $235K to $270K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Bain & Company AI Hiring
Bain & Company has 10 open AI roles right now. They're hiring across AI/ML Engineer, AI Engineering Manager. Positions span Chicago, IL, US, Chicago, IL, Boston, MA, US. Compensation range: $90K - $276K.
Location Context
AI roles in Boston pay a median of $215,000 across 386 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 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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