Senior Data Scientist
New
Source API remote eligibility restrictions: Australia, Canada, New Zealand, United Kingdom, United States Location: EU (Remote) Residing in and legally permitted to work in the EU. Remote-First Flexibility – Work from anywhere in the EUFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 5+ years of experience in Python, with ~10 years of overall software engineering or data experience
- Required Skills
- DockerGraphQLPythonKubernetesMLFlowFastAPICI/CDRESTful APIsGitHub ActionsPrompt EngineeringMLOpsLangChain
Requirements
- 5+ years of experience in Python
- ~10 years of overall software engineering or data experience
- 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
Responsibilities
- 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 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 model inference for latency, cost efficiency, and throughput at scale
- Implement robust monitoring, logging, and alerting for deployed models and services
- Build and maintain CI/CD pipelines for seamless testing, deployment, and iteration of AI systems
- Work closely with product and engineering teams to integrate AI capabilities into core products
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