Senior Manager, AI Engineering
ID du poste
102788
Domaine(s) d’activité
Description et exigences
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’ll join our Data Science & Machine Learning Engineering experts within the AI, Insights & Solutions team. This team is part of Bain’s digital capabilities practice, which includes experts in analytics, engineering, product management, and design. In this multidisciplinary environment, you'll leverage deep technical expertise with business acumen to help clients tackle their most transformative challenges. You’ll work on integrated teams alongside our general consultants and clients to develop data-driven strategies and innovative solutions. Together, we create human-centric solutions that harness the power of data and artificial intelligence to drive competitive advantage for our clients. Our collaborative and supportive work environment fosters creativity and continuous learning, enabling us to consistently deliver exceptional results.
ABOUT BAIN AI, INSIGHTS & SOLUTIONS (AIS)
Bain’s AI, Insights & Solutions (AIS) team works with clients to design and deliver AI-powered solutions that create measurable business impact. You’ll operate in multidisciplinary teams alongside Bain consultants, other experts in product, design, architecture and engineering, and client stakeholders, translating ambiguous business problems into robust AI applications that can be piloted, scaled, and adopted.
THE IMPACT YOU’LL HAVE
Bain works with clients on board-level and executive priorities, helping deliver step-change results across growth, productivity, and resilience. In that context, AI is rarely a point solution. The most meaningful outcomes come from building AI as part of an integrated system that combines technology with redesigned processes, operating model changes, and adoption at scale across the organization.
As a Senior Manager, you will shape how machine learning, data science, and agentic AI are architected and deployed in complex enterprise environments — ensuring that solutions move beyond experimentation into sustained, scalable value creation.
WHERE YOU’LL FIT WITHIN THE TEAM
As a Senior Manager in the Data Science & MLE guild, you will lead the design and delivery of advanced machine learning and agentic AI systems across industries. You’ll combine hands-on technical depth with team leadership and client ownership — guiding multidisciplinary teams while remaining closely involved in architecture, model development, evaluation strategy, and production deployment.
This role sits at the intersection of classical ML, deep learning, GenAI, and emerging agentic systems, with accountability for translating business ambition into scalable, secure, production-grade AI solutions.
WHAT YOU’LL DO
Lead enterprise AI and agentic system design
- Architect and oversee delivery of GenAI and agentic AI applications, from POCs to MVPs toscaledproduction deployments.
- Design LLM-driven applications such as copilots, workflow automation tools, and decision-support systems integrated into enterprise environments.
- Implement robust agentic workflows including tool use, orchestration, routing, memory/state management, human-in-the-loop controls, and clearfailurehandling.
- Design advanced retrieval and knowledge systems (hybrid search, vector databases, knowledge graphs, metadata strategy, reranking, caching, and source attribution).
- Balance performance, reliability, latency, cost, security, and adoption in real-world enterprise settings.
Drive machine learning and data science excellence
- Oversee end-to-end ML lifecycle: data preparation, feature engineering, model selection, training, validation, deployment, and monitoring.
- Applyappropriate methodologiesacross classical ML, deep learning, neural networks, NLP, computer vision, and transformer-based architectures.
- Establish reproducible pipelines with strong experiment tracking, versioning, documentation, and validation practices.
- Design evaluation and observability frameworks for LLM and agentic systems (structured evaluations, regression testing, tracing, automated scoring, and iteration loops).
- Champion best practices in ML engineering,MLOps, andGenAIOps, including CI/CD, containerization, infrastructure-as-code, and environment parity.
Build scalable assets and frameworks
- Develop reusable ML and agentic frameworks, accelerators, templates, and evaluation harnesses that scale across clients.
- Transform prototype code into production-grade, optimized software.
- Ensure secure enterprise deployment withappropriate accesscontrols, responsible AI guardrails, and sensitive data handling.
Lead teams and grow capability
- Lead multidisciplinary ML and data science case teams during client delivery, setting technical direction, reviewing architecture and code, and ensuring high-quality, scalable outcomes.
- Advise and coach ML engineers and data scientists on professional development, providing mentorship, performance feedback, and growth guidance within the Data Science & MLE guild.
- Set technical standards and review key architectural decisions.
- Support AIS leadership in expanding Bain’s ML and AI engineering capabilities.
- Build relationships with keyecosystemand technology partners.
Operate in a client-facing consulting environment
- Partner with consulting teams and senior business leaders to define AI strategies and delivery roadmaps.
- Translate ambiguous businessobjectivesinto scalable analytics and engineering solutions.
- Communicate complex technical concepts clearly to non-technical audiences.
- Contribute to proposal shaping, effort sizing, architecture trade-offs, and risk assessment.
- Travel asrequired(approximately 30%),depending on client needs.
ABOUT YOU
- Advanced degree in Computer Science, Engineering, Statistics, Applied Mathematics, Physics, or related quantitative discipline.
- 10+ years of experience in software engineering, analytics development, or ML engineering.
- 3+ years leading teams of data scientists, ML engineers,orAI engineers.
- Deep understanding of computer science fundamentals, system design, and the full ML and agentic AI lifecycle.
- Strongexpertisein Python and major ML frameworks (e.g., Scikit-learn, TensorFlow,Keras,PyTorch).
- Deepexpertisein neural networks and deep learning, including practical applications in natural language processing and understanding, computer vision, reinforcement learning and transformer-based architectures.
- Experience with agentic frameworks (e.g.,LangGraph,CrewAI, OpenAI Agents,DSPyor similar).
- Experience designing evaluation and observability systems for LLM and agent-based workflows (e.g., OpenAI Evals,LangSmith,ArizePhoenix, Guardrails AI).
- StrongMLOpsexperience (e.g.,MLflow, Kubeflow, CI/CD, Docker, Kubernetes, Terraform).
- Proficiencyin at least one major cloud platform (AWS, GCP, or Azure) and associated ML services.
- Experience with distributed computing frameworks (e.g., Spark, Ray,Dask) and modern data pipelines.
- Strong communicationskills and ability to influence both technical and non-technical stakeholders.
- Fluency in English and Portugueserequired.