Senior Data Scientist (m/f/x)

Develop and Deploy Production-Grade AI Systems

• Build, deploy, and maintain scalable APIs that serve AI/ML models in production environments
• Own end-to-end delivery of AI solutions, from prototyping to fully productionized systems

Design Advanced AI Architectures

• Design and implement Retrieval-Augmented Generation (RAG) systems using vector databases
• Build and orchestrate AI agents using frameworks such as LangGraph, CrewAI, or similar
• Evaluate and select appropriate large language models (LLMs) and foundation models based on specific use cases

Optimize Performance and Scalability

• Continuously optimize model inference for latency, cost efficiency, and throughput at scale
• Identify bottlenecks and implement improvements to ensure high-performing systems

Ensure Reliability and Observability

• Implement robust monitoring, logging, and alerting for deployed models and services
• Ensure system reliability, uptime, and performance through best practices in production ML systems

Establish Engineering Best Practices

• Build and maintain CI/CD pipelines for seamless testing, deployment, and iteration of AI systems
• Contribute to evolving engineering standards as the company transitions from rapid experimentation to more structured, scalable operations

Collaborate Cross-Functionally

• Work closely with product and engineering teams to integrate AI capabilities into core products
• Translate business problems into technical solutions and clearly communicate trade-offs

Drive Structure in a Fast-Paced Environment

• Help bring stability and structure to AI development processes during a high-growth, scale-up phase
• Balance speed of execution with the need for maintainable, well-documented, and scalable systems
• Take ownership of ambiguous problems and drive them through to practical, production-ready outcomes

Core Experience

• 5+ years of experience in Python, with ~10 years of overall software engineering or data experience (candidates transitioning from backend engineering are welcome)
• Proven experience building and deploying production-grade APIs (preferably with FastAPI; REST/GraphQL experience is a plus)
• Hands-on experience working with large language models (LLMs) and LLMOps, including prompt engineering, fine-tuning, and evaluation (e.g., GPT, Claude)
• Strong experience fine-tuning open-source models (e.g., Hugging Face ecosystem)
• Practical experience designing and working with vector databases (e.g., Pinecone, Weaviate, Chroma, pgvector)
• Experience building AI agents using frameworks such as LangGraph, LangChain, CrewAI, or similar
• Solid understanding of model deployment and serving (e.g., vLLM, TGI, or managed endpoints)
• Experience with CI/CD pipelines and modern deployment practices (Docker, Kubernetes, GitHub Actions)
• Strong experience working with and processing large-scale text datasets
• Takes responsibility for outcomes, not just outputs
• Clear communicator: Able to articulate complex technical concepts to both technical and non-technical stakeholders