LLM Dev Analyst
New
SpainFull-TimeMiddle
Salary not disclosed
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Job Details
- Required Skills
- PythonSQLGCPSnowflakeDatabricksPrompt EngineeringLLMLangChainPySpark
Requirements
- Proven experience building applications with Large Language Models (LLMs)
- Strong expertise in prompt engineering
- Strong expertise in RAG architectures
- Strong expertise in agent frameworks (e.g., LangChain, LlamaIndex)
- Experience with Databricks
- Experience with Snowflake
- Experience with GCP
- Experience with PySpark
- Experience with SQL
- Strong proficiency in Python
- Solid understanding of analytics use cases such as A/B testing, marketing performance, or data workflows
- Experience implementing LLM evaluation, monitoring, and guardrails
- Strong problem-solving skills with the ability to translate business needs into technical solutions
Responsibilities
- Design, build, and deploy LLM-powered agents for multiple business domains (A/B testing, marketing analytics, data engineering automation, MarTech workflows)
- Develop multi-step reasoning agents that integrate with internal data systems, APIs, and tools
- Implement RAG architectures to enable agents to leverage enterprise data effectively
- Integrate LLM agents within the Databricks lakehouse architecture
- Build scalable pipelines using PySpark, SQL, and Databricks workflows
- Enable seamless interaction between agents and data warehouses, event streams, and APIs
- Design and optimize prompts, tools, and agent workflows for accuracy and performance
- Develop evaluation frameworks to measure agent quality, reliability, and business impact
- Implement strategies to reduce hallucinations and improve response consistency
- Build agents that automate experimentation analysis (A/B testing insights), marketing performance reporting and optimization, data engineering workflows and monitoring, MarTech processes and campaign operations
- Deliver solutions that drive measurable efficiency gains and decision velocity
- Productionize agent systems with monitoring, logging, and observability
- Implement guardrails, security controls, and governance frameworks
- Ensure scalability, latency optimization, and cost efficiency
- Partner with Data Engineering, Analytics, Marketing, and Product teams
- Translate business requirements into scalable AI solutions
- Communicate insights and capabilities to both technical and non-technical stakeholders
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