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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
As the premier consulting partner for the private equity industry, Bain's PEG boasts a global practice that is over three times larger than any competitor. Our network of over 1,000 professionals supports private equity and institutional investor clients through every stage of the investment life cycle, from deal generation and due diligence to portfolio value creation and exit planning.
Bain \& Company is developing a suite of cutting\-edge data and software solutions designed to revolutionize how the private equity industry uses data for investment insights and decision\-making.
The PEG Innovation team's mission is to create analytical solutions for Bain clients, teams, and the broader institutional investor space using proprietary software and data products. This includes the development, commercialization, and daily management of Bain's proprietary datasets, data, and software businesses.
WHERE YOU’LL FIT WITHIN THE TEAM
AI/ML Engineers on the Diligence Platform build and maintain the data, feature, and retrieval pipelines that power production RAG and ML systems. You work under the guidance of Senior ML Engineers and the Engineering Manager to implement and operate components of the ingestion, embedding, and retrieval stack, ship well\-tested production code, and grow your ownership of these systems over time. You partner with Data Engineers, Data Scientists, and the Agent / AI squad on defined pieces of larger pipelines and RAG workstreams, and you build the habits, tooling fluency, and production judgment expected of a Senior ML Engineer. This is a hands\-on, growth\-oriented engineering role: you are expected to ship reliable, observable code from your first weeks, and to take on increasing ownership as your track record builds.
WHAT YOU'LL DO
Core ML and Data Pipeline Engineering (65%)
- Implement and maintain components of production data and ML pipelines: ingestion jobs, feature and embedding pipelines, and Celery\-based workers, under the direction of senior engineers.
- Build and support pieces of the RAG and retrieval stack: chunking, embedding calls, indexing into pgvector, and basic retrieval and re\-ranking logic, following established patterns.
- Write production\-quality Python: type hints, tests, and linting to the team's standards, with code reviewed by senior engineers before merge.
- Instrument the pipelines and services you own with structured logs and metrics, and help build the dashboards and alerts that make issues visible.
- Reproduce, triage, and fix bugs in pipeline and serving code, escalating ambiguous or high\-severity issues to senior engineers.
Collaboration and Support (25%)
- Partner with Data Engineers, Data Scientists, and the Agent / AI squad on defined tasks within larger pipeline, retrieval, and evaluation workstreams.
- Contribute test cases and sample data to evaluation harnesses and golden datasets, under the direction of senior engineers.
- Participate in design reviews and code reviews, both as reviewer and reviewee, building judgment about production trade\-offs.
- Keep runbooks, READMEs, and pipeline documentation current as you build and change the systems you touch.
Other (10%):
- Use AI coding assistants to accelerate scaffolding and boilerplate, and review generated code against team standards before committing.
- Use LLMs to draft documentation and status notes; validate and refine outputs before sharing them.
- Take on interviewing and hiring\-loop participation as your experience grows.
ABOUT YOU
- Bachelor's degree in Computer Science, Engineering, Machine Learning, Data Science, Statistics, or a related field (or equivalent practical experience).
- 2\+ years of experience building software, data, or ML systems, ideally including some exposure to production pipelines or services.
- Exposure to model deployment, serving, or monitoring is a plus.
- Experience working with structured feedback and code review, and a track record of improving code quality over time.
- Experience collaborating with Data Engineers, Data Scientists, or the Agent / AI squad to ship features that depend on retrieval or ML outputs.
- Comfort with Python as a primary language; exposure to a modern cloud environment (Databricks, Azure, or AWS) is a plus.
- Demonstrated ability to take a well\-scoped task from specification to a tested, reviewed implementation with limited supervision.
ML engineering / LLMOps
- Working knowledge of Python for data and ML workloads: type hints, Pydantic, pytest, Ruff, with production\-quality pipeline and serving code that would pass a code review.
- Familiarity with MLflow concepts: experiment tracking, model registry, and promotion workflows.
- Exposure to LLMOps concepts: prompt versioning, model gateways (e.g., Portkey), and inference orchestration frameworks (LangChain, LlamaIndex, or equivalent).
- Good understanding of model\-serving concepts: latency, throughput, and batching, even without direct production ownership yet.
- RAG pipeline building blocks: chunking strategies, embeddings, and vector stores such as pgvector; able to contribute to indexing and retrieval jobs under senior guidance.
- Understanding of model and pipeline evaluation basics: what a golden dataset is, and why regression gates matter in CI.
- Docker: comfortable containerising pipeline or serving code and running it locally for testing.
- Git: confident with PR\-based workflows.
Generative AI and agentic systems
- Contributes to inference and retrieval services that feed agent workflows as structured tool responses.
- Supports RAG quality work: helps build and run recall and precision checks against defined benchmarks.
- Exposure to LLM\-as\-judge evaluation patterns, even if applied under senior\-engineer direction.
General
- Treats testing, observability, and documentation as part of the job, not an afterthought, even on smaller tasks.
- Raises questions and surfaces uncertainty early rather than guessing silently on ambiguous requirements.
- Uses AI tooling to move faster, and reviews all generated code and documentation critically before it enters the codebase.
- Communicates clearly with teammates about progress, blockers, and trade\-offs; asks for help early.
- This role follows a hybrid model, requiring in\-office presence at least 1 day per week
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 be included in job postings for open roles. The estimated annualized compensation for this role is as follows:
In Atlanta, the good\-faith, reasonable annualized full\-time salary range for this role is between $79,250 \- $86,500
In Texas, the good\-faith, reasonable annualized full\-time salary range for this role is between $83,313 \- $90,750
In Chicago, the good\-faith, reasonable annualized full\-time salary range for this role is between $87,438 \- $95,250
Placement within these ranges will vary based on factors such as experience, education, training, and skill level.
Compensation also includes a discretionary annual performance bonus, 401(k) plan with employer contribution, and Bain’s best\-in\-class benefits—including full premium coverage for medical, dental, and vision, generous paid time off, and more.
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
Salary Context
This $79K-$90K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($85K) sits 60% below the category median. Disclosed range: $79K to $90K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Bain & Company AI Hiring
Bain & Company has 4 open AI roles right now. They're hiring across AI/ML Engineer, AI Engineering Manager. Positions span Houston, TX, US, Dallas, TX, US, Chicago, IL, US. Compensation range: $90K - $178K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 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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