Impress employers and recruiters.
Choose from hundreds of resume examples.
Impress employers and recruiters.
Choose from hundreds of resume examples.
Target Resume Now
Tailor your resume to this AI Engineering, Intern (Berlin / Munich) role.
Rezi rewrites your resume against Bain & Company's job description. Free.
Tailor your resume to this AI Engineering, Intern (Berlin / Munich) role.
Rezi rewrites your resume against Bain & Company's job description. Free.
Don't guess if your resume is good enough.
See how it scores against the AI Engineering, Intern (Berlin / Munich) posting at Bain & Company — free, in seconds.
Don't guess if your resume is good enough.
See how it scores against the AI Engineering, Intern (Berlin / Munich) posting at Bain & Company — free, in seconds.
About the Role
The AI Engineering Intern will contribute to the design and development of generative AI applications and agentic solutions for Bain’s clients. You will gain hands-on exposure to the full AI development lifecycle, from early prototyping through to production-ready deployment, in a learning-first role with mentorship and increasing responsibility.
Responsibilities
- Contribute to the design and development of GenAI applications using modern LLM stacks.
- Support the implementation of AI pipelines and components, including Retrieval-Augmented Generation (RAG), fine-tuning, embedding generation, and hybrid retrieval strategies.
- Assist in implementing tool use, function calling, and orchestration across AI workflows.
- Assist in building agent capabilities including context engineering, memory and state management, orchestration, and tool integration.
- Support model experimentation, evaluation design, and production deployment tasks.
- Write clean, testable code and contribute to APIs and services through the full SDLC.
- Contribute to evaluation and observability for GenAI systems.
- Gain exposure to responsible AI practices across system design, including implementing guardrails and human-in-the-loop workflows.
- Contribute to evaluation harnesses and reusable components.
- Contribute to ML solutions end-to-end, including data preparation, model selection, training, validation, and performance analysis.
- Apply appropriate ML methods spanning classical ML and deep learning.
- Develop working knowledge of transformer fundamentals and LLM concepts.
- Collaborate with product, engineering, and data science teammates to build well-structured, reliable AI systems.
- Communicate clearly in working sessions, demos, and documentation.
- Support client discussions and documentation as part of broader engagement teams.
Requirements
- 0–2+ years of professional AI / ML engineering experience, or equivalent through internships, research, academic projects, or open-source contributions.
- Currently pursuing or recently completed a bachelor’s, master’s, or PhD in CS, ML, AI, Data Science, Engineering, or a related quantitative field; or equivalent early-career experience.
- Strong academic performance or demonstrated excellence through research, projects, internships, competitions, or open-source contributions.
- Proficiency in Python and some experience building APIs or services, or comparable project-based experience.
- Experience building complex, multi-stack generative AI programs from conception through production.
- Exposure to retrieval and search systems and familiarity with structured and unstructured data stores.
- Exposure to agentic patterns; hands-on experience preferred.
- Strong engineering practices: testing, code review, version control, CI/CD, and performance profiling.
- Excellent interpersonal and communication skills; interest in applying AI to real-world business problems in a client-facing environment.
- Fluency in English and German is required.
Skills
- Python
- APIs
- Services (e.g., REST/gRPC)
- Generative AI
- LLM stacks
- Retrieval-Augmented Generation (RAG)
- Fine-tuning
- Parameter-efficient tuning
- Embedding generation and optimization
- Hybrid retrieval strategies (vector, graph, keyword)
- Tool use
- Function calling
- Orchestration
- Agent capabilities
- Context engineering
- Memory and state management
- Model experimentation
- Evaluation design
- Production deployment
- SDLC
- Evaluation and observability for GenAI systems
- Responsible AI practices
- Guardrails
- Fallbacks
- Human-in-the-loop (HITL) workflows
- ML solutions
- Data preparation
- Feature engineering
- Model selection
- Training
- Validation
- Testing
- Performance analysis
- Classical ML
- Deep learning
- Transformer fundamentals
- LLM concepts
- Retrieval and search systems
- Vector search
- Hybrid retrieval
- Reranking
- Structured and unstructured data stores
- Agentic patterns
- Code review
- Version control
- CI/CD
- Performance profiling
Location
- Berlin
- Munich
Work Type
- Intern (Full-Time)
- Hybrid (minimum three days per week in person)
Experience Level
- Intern
- 0-2+ years of professional AI / ML engineering experience
Education Level
- Bachelor’s, Master’s, or PhD in CS, ML, AI, Data Science, Engineering, or a related quantitative field
About the Company
- We are proud to be consistently recognized as one of the world’s best places to work, currently the top ranked consulting firm on Glassdoor’s Best Places to Work list.
- Extraordinary teams are at the heart of our business strategy, requiring intentional focus on bringing together diverse 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.
- Bain’s AI, Insights & Solutions (AIS) team works with clients to design and deliver AI-powered solutions that create measurable business impact.
- Bain offers significant learning and growth opportunities through our apprenticeship model.
- Bain works with clients on board-level and executive priorities.
- Bain collaborates with major AI ecosystem partners through its partnerships.