Senior AI Engineer
New
J
JobgetherAI / Software Development
Based in GermanyFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 5+ years of professional software development experience, including at least 2 years focused on AI/ML engineering
- Required Skills
- PythonCloud ComputingMachine LearningFastAPIMLOpsGenerative AI
Requirements
- 5+ years of professional software development experience, including at least 2 years focused on AI/ML engineering.
- Demonstrated experience building and deploying production-grade AI/ML systems.
- Strong practical experience with LLMs and generative AI, including prompt engineering, fine-tuning, RAG, or LLM orchestration frameworks.
- Hands-on experience with vector databases and semantic search technologies such as Pinecone, Weaviate, Qdrant, Elasticsearch, or OpenSearch.
- Proven understanding of RAG architectures, retrieval pipelines, and evaluation approaches for LLM-based systems.
- Experience designing and managing data pipelines for AI/ML applications.
- Strong Python programming skills, including practical experience with FastAPI and asynchronous programming.
- Experience with major cloud platforms such as AWS, Azure, or Google Cloud.
- Proficiency in database management, data wrangling, distributed systems, algorithm design, statistics, and data science.
- Familiarity with MLOps practices, including ML CI/CD, monitoring, and experiment tracking.
- Excellent communication, presentation, and documentation skills.
Responsibilities
- Design, build, and deploy AI solutions focused on LLMs, generative AI, and modern machine learning to solve real-world business challenges.
- Own the end-to-end lifecycle of AI features, including problem definition, data understanding, modeling, evaluation, deployment, monitoring, and iteration.
- Develop and optimize LLM-powered applications such as RAG systems, AI agents, chatbots, and document-understanding solutions.
- Design and contribute to data pipelines supporting efficient data collection, preprocessing, analytics, and AI/ML workflows.
- Deploy AI solutions within cloud infrastructure, ensuring scalability, security, reliability, and strong performance.
- Collaborate with cross-functional teams to integrate AI capabilities into products, services, and internal tools.
- Identify and prioritize opportunities where AI and machine learning can deliver measurable improvements across business processes and operations.
- Educate and advise stakeholders on AI capabilities, use cases, and best practices while helping foster an AI-first culture.
- Continuously improve systems, processes, and ways of working while maintaining high standards for engineering quality.
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