Principal Data Solutions

Design, develop and maintain user interfaces for our web applications in Pharma research and clinical domains.
Design, develop and maintain scalable data solutions supporting research, drug discovery and clinical development.
Design and integrate data pipelines and services across relational, NoSQL, scientific and unstructured data sources.
Collaborate with scientists and data scientists to operationalize analytical models, ML solutions and scientific workflows.
Develop GenAI and RAG solutions using LLMs, embeddings, vector search and enterprise knowledge sources.
Drive agentification of R&D and data science workflows using AI agents, tool calling, MCP and multi-step orchestration.
Build cloud-native AI and data solutions using AWS or using Azure services.
Implement secure interaction between AI agents and scientific data, APIs, analytical tools and enterprise applications.
Apply human-in-the-loop, guardrails, evaluation and monitoring to ensure AI solutions are reliable, traceable and appropriate for R&D use.
Optimize applications, data pipelines and AI workloads for scalability, performance and cost.
Maintain architecture, data flow, API and solution documentation throughout the development lifecycle.

Bachelor's or Master's degree in Computer Science, Software Engineering, or a related field with a minimum of 12 years of industry experience in software development, with a focus on Cloud technologies.
Strong Python development skills with hands-on experience in FastAPI, Flask, Streamlit and REST APIs.
Strong SQL and database experience with PostgreSQL, PLSQL, along with exposure to NoSQL and vector databases.
Experience building data pipelines and data-intensive applications supporting analytical or data science workloads.
Hands-on experience with GenAI, LLM integration, prompt engineering, structured outputs and function/tool calling.
Experience with RAG, embeddings, semantic search, vector databases and grounding LLMs with enterprise or scientific data.
Experience developing agentic AI solutions using frameworks such as LangGraph, LangChain, Semantic Kernel or equivalent.
Understanding of agentification, including agent orchestration, MCP, state/context management, tool selection and human-in-the-loop workflows.
Hands-on cloud-native development experience on AWS and/or Azure, including serverless, containers, managed databases, storage, API and AI services.
Experience with Amazon Bedrock or Azure AI/OpenAI capabilities for developing enterprise GenAI and agentic solutions is good to have.
Understanding of AI evaluation, observability, groundedness, traceability, guardrails and responsible AI practices.

Principal Data Solutions

Design, develop and maintain user interfaces for our web applications in Pharma research and clinical domains.
Design, develop and maintain scalable data solutions supporting research, drug discovery and clinical development.
Design and integrate data pipelines and services across relational, NoSQL, scientific and unstructured data sources.
Collaborate with scientists and data scientists to operationalize analytical models, ML solutions and scientific workflows.
Develop GenAI and RAG solutions using LLMs, embeddings, vector search and enterprise knowledge sources.
Drive agentification of R&D and data science workflows using AI agents, tool calling, MCP and multi-step orchestration.
Build cloud-native AI and data solutions using AWS or using Azure services.
Implement secure interaction between AI agents and scientific data, APIs, analytical tools and enterprise applications.
Apply human-in-the-loop, guardrails, evaluation and monitoring to ensure AI solutions are reliable, traceable and appropriate for R&D use.
Optimize applications, data pipelines and AI workloads for scalability, performance and cost.
Maintain architecture, data flow, API and solution documentation throughout the development lifecycle.

Bachelor's or Master's degree in Computer Science, Software Engineering, or a related field with a minimum of 12 years of industry experience in software development, with a focus on Cloud technologies.
Strong Python development skills with hands-on experience in FastAPI, Flask, Streamlit and REST APIs.
Strong SQL and database experience with PostgreSQL, PLSQL, along with exposure to NoSQL and vector databases.
Experience building data pipelines and data-intensive applications supporting analytical or data science workloads.
Hands-on experience with GenAI, LLM integration, prompt engineering, structured outputs and function/tool calling.
Experience with RAG, embeddings, semantic search, vector databases and grounding LLMs with enterprise or scientific data.
Experience developing agentic AI solutions using frameworks such as LangGraph, LangChain, Semantic Kernel or equivalent.
Understanding of agentification, including agent orchestration, MCP, state/context management, tool selection and human-in-the-loop workflows.
Hands-on cloud-native development experience on AWS and/or Azure, including serverless, containers, managed databases, storage, API and AI services.
Experience with Amazon Bedrock or Azure AI/OpenAI capabilities for developing enterprise GenAI and agentic solutions is good to have.
Understanding of AI evaluation, observability, groundedness, traceability, guardrails and responsible AI practices.

Principal Data Solutions

Skills