AI Engineer – GenAI & Life Sciences (Remote 100%) (H/M/X)

AI Engineer – GenAI & Life Sciences (Remote from Spain)

Why this role matters

You will play a key role in transforming complex molecular and pharmaceutical data into actionable insights using advanced Machine Learning and Generative AI. Your work will directly support drug discovery, portfolio decision-making, and competitive intelligence in a real-world life sciences context.

You will join an international, highly technical environment where rapid prototyping, hands-on development, and practical impact matter more than theory alone.

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What you will gain in this role

• High-impact AI projects in life sciences
  You will work on real pharmaceutical challenges, applying GenAI, RAG systems, and ML models to molecular data, transcriptomics, and competitive intelligence workflows.

• End-to-end ownership of AI solutions
  From prototyping to production, you will design, build, deploy, and optimize AI systems that are actually used in decision-making processes.

• Cutting-edge Generative AI exposure
  You will work extensively with LLMs, agentic AI systems, AWS Bedrock, and modern information retrieval architectures.

• Strong collaboration with domain experts
  You will collaborate closely with life sciences specialists, ensuring that AI solutions are aligned with scientific and business realities.

• Fully remote work with European team alignment
  You can work 100% remotely, as long as you are based in Spain to align with the European time zone.

• Continuous learning and technical growth
  You will stay close to the latest advances in GenAI, ML, and pharmaceutical AI, with space to experiment and apply new ideas.

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Your mission

Your mission will be to design and implement production-ready AI systems that turn complex, unstructured life sciences data into knowledge that supports smarter pharmaceutical decisions.

You will contribute to building scalable, ethical, and reliable AI solutions that accelerate research, improve portfolio strategy, and strengthen competitive intelligence across the organization.

In this role, you will:

• Design and build Generative AI applications such as RAG systems, agentic workflows, and information retrieval solutions for molecular and pharmaceutical data.

• Develop and deploy machine learning models for life sciences use cases, including transcriptomics and unstructured scientific data.

• Create, maintain, and query knowledge graphs to support portfolio management and competitive intelligence.

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What will help you succeed in this position

• Strong Python expertise applied to real systems
  Your advanced Python skills will allow you to build robust ML models, APIs, and AI services that move smoothly from prototype to production.

• Solid foundations in Machine Learning
  A deep understanding of ML concepts, including deep learning architectures and attention mechanisms, will help you design better solutions rather than just use tools.

• Hands-on experience with GenAI and RAG systems
  Your experience building and productionizing RAG pipelines, agentic systems, and information retrieval solutions will be key to delivering impact quickly.

• Experience with cloud-based AI deployment
  Working with AWS (especially AWS Bedrock), APIs built with FastAPI, and cloud infrastructure will allow you to scale and maintain AI services reliably.

• Ability to work with complex, unstructured data
  You are comfortable handling scientific documents, molecular data, and noisy datasets, turning them into structured knowledge.

• Clear communication and collaboration skills
  Your ability to explain technical concepts to non-technical stakeholders will help align AI solutions with real business needs.

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Technical background that supports your success

Core experience

• Bachelor’s or Master’s degree in a quantitative field such as mathematics, computer science, computational biology, or bioinformatics

• Advanced Python development

• GenAI tools with a strong focus on AWS Bedrock

• Agentic AI systems and frameworks such as LangChain or LangGraph

• RAG systems and information retrieval solutions

• Knowledge graph construction and querying

• API development with FastAPI or similar frameworks

• CI/CD pipelines and MLOps practices