Agentic Infrastructure & Engineering Jobs

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62 of 472 matching · page 1 of 2

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TypeAgent infrastructure126 Agent engineering153 Developer tooling2 Applied research11 Forward deployed179 Other1

ModeRemote92 Hybrid95 On-site72 Location TBD213

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62 of 472 matching · page 1 of 2

Lead, AI Engineering
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São Paulo, São Paulo, Brazil
1d agoTop pickNew
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Forward deployedLeadTop pickNew

Lead, AI Engineering

Bain & CompanySão Paulo, São Paulo, Brazil

Posted 1d ago/Full-time/Expires Nov 10, 2026

Apply on company site

Why it matters

This role ships client-facing agentic systems, including multi-agent workflows, reasoning, tool use, memory architecture, retrieval, guardrails, human review, and automated evaluation. The work spans prototypes through production deployments across enterprise client environments.

About the job

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 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.

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 an AI Engineer in AIS, you will build the technical core of these transformations and work as part of broader Bain consulting teams to move solutions from prototype to real adoption. The result is measurable impact at the company or enterprise level and, in many cases, helps clients set new performance standards for their industries.

The Role

The Lead AI Engineer will design, build, and ship generative AI systems and agentic solutions for Bain’s clients. You will contribute across the full development lifecycle — from early experimentation and prototyping through to production deployment — collaborating closely with senior engineers, product managers, and data scientists. This is a hands-on individual contributor role with growing influence on technical direction and an opportunity to begin mentoring more junior team members.

You will have opportunities to work with major AI ecosystem partners through Bain’s partnerships, collaborating on real client deployments and helping shape how emerging capabilities are applied in enterprise settings.

Bain offers significant learning and growth opportunities through the breadth and depth of problems we solve, the level of impact we help clients achieve, and our apprenticeship model. You will learn by doing, with support from experienced teammates, frequent feedback, and increasing responsibility over time.

What You’ll Do

Contribute to the design, development, and deployment of end-to-end generative AI systems, including multi-agent workflows and production-grade AI applications.
Build and iterate on multi-component AI pipelines, including:
Retrieval-Augmented Generation (RAG)
Fine-tuning and parameter-efficient tuning
Embedding generation and optimization
Hybrid retrieval strategies (vector, graph, keyword)
Implement reasoning, tool use, function calling, and orchestration across AI workflows
Build and contribute to agentic systems, applying sound engineering principles around separation of concerns, memory architecture, and tool integration
Contribute across the full stack: model experimentation, evaluation design, and production system deployment
Build and maintain APIs, microservices, CI/CD pipelines, and cloud-native deployments with attention to observability and reliability
Support and help build GenAIOps processes for automated testing, regression evaluation, latency monitoring, and continual improvement
Balance performance, safety, responsible AI principles, and cost across system design:
Implement guardrails, fallbacks, red-teaming strategies, and human-in-the-loop (HITL) workflows
Partner with global ethics teams to ensure alignment with Bain’s Responsible AI standards
Build automated evaluation suites integrating user signals, continual learning cycles, and ongoing model updates
Design and implement evaluation frameworks covering:
Hallucination rate and factual consistency
Relevance and precision/recall
Latency, throughput, and system-level performance
Cost tracking and efficiency
Partner closely with product, engineering, data science, ethics, and infrastructure teams to build robust, compliant AI systems
Contribute technical insights and communicate findings clearly to cross-functional stakeholders and, where relevant, clients
Share knowledge with peers and support a culture of technical learning around RAG, agents, prompt engineering, and AI safety

What We’re Looking For (Qualifications)

3–5+ years in software engineering, ML engineering, or applied AI roles with hands-on building responsibilities
Demonstrated experience shipping generative AI features or systems end-to-end, from prototyping through production
Clear communication skills with the ability to explain technical concepts to non-technical collaborators and stakeholders
Demonstrated ability to collaborate effectively across engineering, product, and data science teams; some experience supporting or informally mentoring peers is a plus
Solid prompt engineering and context engineering skills; familiarity with conversation design principles
Working knowledge of evaluation design, experimentation frameworks, and data labeling strategies for LLM applications
Experience with:
RAG architectures (vector-based retrieval; exposure to hybrid or graph-based approaches is a plus)
Agentic patterns (tool use, routing, memory management; multi-agent systems experience is a plus)
ReAct, RLAIF, and other HITL + feedback loops.
AI-Specific Tools & Frameworks - Orchestration frameworks, Vector and graph databases and Model + API ecosystems
Strong background in system design, architecture, and production-grade deployment
Familiarity with cost and latency tradeoffs when working with LLM workloads
Comfort operating in high-ambiguity environments with collaborative cross-functional teams
Eagerness to grow into a technical leadership role and support junior team members
Experience in client-facing consulting or enterprise transformation environments is a strong plus

Working Model & Travel

This role requires a minimum of three days per week working together in person, either at a client location or at your Bain home office.
Travel is required beyond your home office / primary working location. Frequency and destination vary by project needs.

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