Note: The job is a remote job and is open to candidates in USA. Insight Global is partnering with a global pharmaceutical research leader to transform drug discovery through biological datasets, cloud technologies, advanced analytics, and artificial intelligence. The AI Application Engineer will develop AI-powered applications and agentic systems that help researchers interact with complex biological and research data, while building scalable, reliable solutions across cloud and life sciences environments.
Responsibilities
- Design, develop, and deploy AI-powered applications and intelligent agent systems
- Build integrations with large language models (LLMs) and external tools/services
- Develop applications leveraging Model Context Protocol (MCP) and modern agent frameworks
- Create tool-calling workflows and agent orchestration capabilities
- Collaborate with data scientists, researchers, and engineers to deliver AI-enabled solutions
- Develop scalable APIs supporting AI and machine learning workloads
- Deploy and manage applications within Google Cloud Platform (GCP)
- Build and maintain containerized applications using Docker and Kubernetes
- Optimize performance, reliability, testing, and validation of AI systems
- Support the development of AI-driven research assistants and scientific discovery tools
Skills
- 2+ years of professional experience developing and deploying AI, Machine Learning, or Generative AI solutions
- Strong programming experience with Python and/or TypeScript
- Experience integrating and deploying LLMs in production environments
- Hands-on experience building AI agents and agent-based applications
- MCP (Model Context Protocol)
- Pydantic AI, LangGraph, or similar agent frameworks
- Tool/function-calling architectures
- OpenAI, Claude, or comparable LLM ecosystems
- 2+ years of experience working within Google Cloud Platform (GCP)
- Strong experience with Docker and containerized deployments
- Experience building APIs with FastAPI or similar frameworks
- Experience with Retrieval-Augmented Generation (RAG) architectures
- Knowledge of agent orchestration frameworks and multi-agent systems
- Integration testing, load testing, and performance optimization experience
- Experience supporting scientific, research, or data-intensive environments
- Drug Discovery
- Bioinformatics
- Biology
- Healthcare AI
- Life Sciences
Benefits
- Remote work model
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