Developer

Project SummaryThis project aims to transform knowledge graphs, Retrieval-Augmented Generation (RAG) pipelines, and enterprise data into AI-driven personas that enable smarter pharmaceutical launch planning, enhance customer experiences, and support evidence-based decision-making. By leveraging advanced AI and data intelligence capabilities, the platform delivers actionable insights, improves stakeholder engagement, and helps drive more effective commercial and strategic outcomes across the pharmaceutical value chain.Contract Length: 6 monthsStart Date: ASAPLocation: Fully remoteLanguage: English speakingKey Technologies• Azure AI components, Azure OpenAI, Amazon Bedrock, OpenAI, Gemini, and Anthropic• Agentic workflows, LangChain, LangFuse,Haystack, prompt orchestration, tracing, and evaluation• RAG, GraphRAG, embeddings, vector search, semantic chunking, knowledge graphs• Snowflake and enterprise data platforms for structured and semi-structured data• Python, Rust, TypeScript, Node.js, FastAPI, Java, PostgreSQL• Docker, Openshift, Kubernetes and CI/CDRequired Qualifications• Strong professional experience in software engineering, preferably with production-grade AI/ML or data-driven platforms.• Hands-on experience with LLM applications, RAG pipelines, AI orchestration, and modern backend development.• Good understanding of knowledge graphs, vector search, embeddings, chunking strategies, and unstructured data processing.• Experience with cloud-native development, APIs, microservices, testing, CI/CD, and scalable system design.• Ability to communicate complex technical topics clearly to both technical and non-technical stakeholders.Preferred Skills• Experience with LangChain, LangFuse, agentic AI tooling, prompt tracing, evaluation, and observability.• Experience with Azure AI components, Amazon Bedrock, Snowflake, ArangoDB, and multi-provider LLM orchestration.• Experience with OCR, document intelligence, data extraction, or transformation of unstructured content into structured knowledge assets.• Experience with document chunking strategies, semantic chunking, metadata enrichment, retrieval optimization, and improving context quality for RAG-based systems.• Nice to have - Experience in pharma, healthcare, biomedical data, launch planning, or customer experience use cases.Working SetupThe role is part of an agile product delivery setup with two-week sprints, regular team ceremonies, and close collaboration with BI product, architecture, and business stakeholders.