> Markdown version of [/jobs/ext/1499203-ml-staff-engineer-llm-production-systems](https://www.wearedevelopers.com/jobs/ext/1499203-ml-staff-engineer-llm-production-systems). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # ML Staff Engineer - LLM & Production Systems - **Company:** Bain & Co. - **Location:** New York, NY, United States - **Experience:** Expert - **Salary:** $141,000.0 - $169,000.0 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Automated Storage and Retrieval Systems, Cloud Computing, Continuous Integration, Data Integration, Python (Programming Language), Machine Learning, Open Source Technology, Performance Tuning, Regression Testing, Azure Machine Learning, Software Deployment, Software Engineering, SQL Databases, Enterprise Software Applications, Large Language Models, Model Validation, Reliability of Systems, Information Technology, Deployment Automation, Machine Learning Operations - **Published:** July 30, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=28bfd4060d10ee9a ## About the Role * Bachelor's degree in Computer Science, Engineering, Data Science, or a related field, or equivalent practical experience * 6+ years of experience in software engineering, machine learning engineering, or related technical roles * Experience designing and operating production-grade ML systems at scale * Experience deploying machine learning or LLM-powered applications into production environments * Strong proficiency in Python, SQL, and production software development * Experience designing scalable ML pipelines, inference systems, and retrieval architectures * Experience implementing CI/CD practices and MLOps frameworks * Deep understanding of NLP, transformer architectures, retrieval systems, fine-tuning, and structured extraction * Experience building cloud-based ML infrastructure * Strong analytical, communication, and problem-solving skills * Proven ability to mentor engineers and influence technical direction * Advanced English proficiency (written and spoken) Preferred Qualifications * Advanced degree in Computer Science, Machine Learning, or a related technical discipline * Experience owning machine learning platforms supporting multiple teams or products * Experience operating LLM-powered systems with measurable business impact * Experience establishing engineering standards across ML organizations * Experience designing large-scale entity resolution or data integration systems ## Description 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'll join our Enterprise Technology organization, partnering closely with engineering, product, and data teams to build the next generation of AI- and machine learning-powered solutions across Bain. Working in a highly collaborative environment, you'll help define the technical direction of ML platforms that enable scalable, reliable, and impactful solutions for internal users and client-facing products. ------------------------- WHERE YOU'LL FIT WITHIN THE TEAM As a Staff Engineer, Machine Learning, you'll play a critical role in defining the architecture, engineering standards, and operational excellence of Bain's machine learning ecosystem. You'll partner with cross-functional teams to build scalable ML and LLM-powered systems, establish engineering best practices, and translate complex business challenges into robust technical solutions. This role is ideal for someone who enjoys solving complex engineering problems, influencing technical strategy, and mentoring other engineers while remaining hands-on with modern AI technologies. ------------------------- WHAT YOU'LL DO Architect & Build ML Systems * Design and evolve scalable machine learning pipelines supporting analytics, Q&A, and insight generation across products * Select and implement appropriate ML and LLM architectures based on quality, latency, scalability, and cost considerations * Design resilient systems capable of handling evolving datasets while continuously improving model performance Deploy & Operate Production ML Platforms * Lead production deployment of LLM-powered systems using both open-source models and commercial APIs * Define service level objectives (SLOs) and ensure system reliability, scalability, and operational efficiency * Develop deployment strategies and optimize inference workloads through capacity planning Own ML Platforms End-to-End * Lead the design, implementation, and long-term operation of business-critical ML systems * Define operational KPIs and engineering standards * Lead root cause analysis and postmortems while driving continuous improvements * Translate complex business needs into scalable engineering solutions Improve Data Quality & Model Performance * Establish robust data contracts and monitoring practices * Build evaluation frameworks including dashboards, regression testing, and slice analysis * Continuously improve model quality while preventing regressions Advance MLOps & Engineering Excellence * Define lifecycle management for data, models, and prompts * Establish CI/CD standards and deployment best practices for ML systems * Improve observability, governance, and production monitoring * Mentor engineers and elevate technical standards across teams ## Related Videos - [Fault Tolerance and Consistency at Scale: Harnessing the Power of Distributed SQL Databases](https://www.wearedevelopers.com/videos/1146-fault-tolerance-and-consistency-at-scale-harnessing-the-power-of-distributed-sql-databases) - [Green Cloud Computing](https://www.wearedevelopers.com/videos/592-green-cloud-computing) - [Introduction to Responsible AI: Balancing Value and Risk](https://www.wearedevelopers.com/videos/1972-introduction-to-responsible-ai-balancing-value-and-risk) - [Effective Machine Learning - Managing Complexity with MLOps](https://www.wearedevelopers.com/videos/185-effective-machine-learning-managing-complexity-with-mlops) - [Fault Tolerance and Consistency at Scale: Harnessing the Power of Distributed SQL Databases](https://www.wearedevelopers.com/videos/1520-fault-tolerance-and-consistency-at-scale-harnessing-the-power-of-distributed-sql-databases) - [Give Your LLMs a Left Brain](https://www.wearedevelopers.com/videos/1160-give-your-llms-a-left-brain) ## Related Articles - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [Who Owns Your Content in the Age of LLMs?](https://www.wearedevelopers.com/magazine/610-who-owns-your-content-in-the-age-of-llms) - [The Most Popular IT Jobs on the Market](https://www.wearedevelopers.com/magazine/376-the-most-popular-it-jobs-on-the-market)