AI Application Engineer – AI Products (LLM & RAG) (m/f/d)
Machine Learning Reply · Berlin, Germany
What this role requires
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Job description
At Machine Learning Reply, we help organizations turn cutting-edge AI technologies into real-world applications and scalable digital products.
To strengthen our team, we are looking for an AI Application Engineer who enjoys building AI-powered solutions and intelligent product features using modern machine learning and generative AI technologies.
While our GenAI Engineers focus on model development and AI architectures, AI Application Engineers focus on building user-facing AI applications and turning AI capabilities into scalable products.
In this role, you will work at the intersection of AI engineering, backend development, product development and cloud deployment, building production-ready AI systems that create real business value.
Tasks
As an AI Application Engineer, you design and build AI-powered applications and product features for enterprise clients.
Read the full description (38 more sections)Show less
Your projects may include:
Designing and developing AI applications, such as enterprise assistants, AI copilots, semantic search platforms and intelligent automation systems
Building LLM-powered applications using Retrieval-Augmented Generation (RAG) and modern AI frameworks
Developing end-to-end AI products, integrating LLM APIs, enterprise data sources and backend services
Designing scalable AI microservices and APIs to integrate AI capabilities into enterprise platforms
Implementing vector search, embeddings pipelines and knowledge retrieval systems
Rapidly prototyping AI product features and proof-of-concepts and evolving them into production systems
Collaborating closely with product managers, designers, AI engineers and enterprise customers to develop impactful AI solutions
Deploying AI systems to cloud platforms and production environments using modern DevOps practices
Ensuring reliable, scalable and observable AI services through CI/CD pipelines, monitoring and containerized deployments
Benefits
Work in an open and collaborative environment within the global Reply network and build next-generation AI applications and intelligent digital products
Collaboration with interdisciplinary teams including AI engineers, software developers and data scientists across industries such as Banking, Insurance, Automotive and Retail
A very active social program including paid training, conferences, communities of practice, hackathons and Reply XChange
Monetary Benefits include: Mobility package, Gym subsidy & WellPass, Insurance & Pension Scheme, Corporate Savings Plan, KiTa and Childcare Allowance
Flexible work arrangement between home office, EU-wide workation options, on site office-work in our downtown Munich office with access to Stammstrecke, and client on site visits
Requirements
Degree in Computer Science, Software Engineering, Data Science or a comparable technical field
Convincing communication and presentation skills in German and English in order to participate in workshops of both languages
Strong programming skills in Python and modern backend frameworks
Experience building applications using AI, machine learning or generative AI technologies
Familiarity with Retrieval-Augmented Generation (RAG) and vector databases
Familiarity with cloud platforms such as AWS, Azure or GCP
Engaging directly with enterprise clients to understand their business challenges and identify high-impact opportunities for AI-driven solutions
Nice to have
Experience running technical workshops or facilitating solution design sessions
Experience developing APIs, microservices and scalable backend systems, including vector databases
Experience with containerization and DevOps practices (Docker, CI/CD pipelines, Kubernetes or similar)
Experience with frameworks such as LangChain, LlamaIndex or HuggingFace
Experience deploying AI services in cloud environments
Knowledge of AI observability, monitoring, and evaluation of LLM systems
Example projects you may work on
Enterprise AI knowledge assistants
AI copilots for internal business tools
Semantic search platforms for enterprise data
Document intelligence systems powered by LLMs
AI agents and automation systems for enterprise workflows
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