礼来 Lilly
LAL Digital and Automation Integration Principal Engineer
医疗医药
高科技/人工智能
上海
5-10年
本科
¥50 - 80K13薪
职位描述
Key Responsibilities
Digital Solution Development & System Integration
Design, develop, and maintain software solutions, data pipelines, and APIs that integrate LIMS, ELN, lab automation platforms, and analytical instruments into unified digital workflows.
Architect and implement cloud-native integration services and event-driven microservices on AWS/Kubernetes that orchestrate instrument scheduling, sample tracking, and experimental data capture in real time.
Develop RESTful/gRPC APIs and instrument adapters that expose lab hardware and data capabilities to AI agent frameworks and other consuming systems.
Build robust data pipelines that ingest, transform, validate, and route experimental data between scientific systems and agentic AI decision engines.
Ensure integrations are production-quality: well-tested, version-controlled, observable, and maintainable.
Agentic AI And Automation Integration
Collaborate with Frontier AI and AI@Lilly teams to design and implement the integration architecture connecting multi-agent systems with lab automation execution layers enabling agents to reason over experimental data, trigger automated actions, and adapt protocols autonomously.
Design and implement tool interfaces, function-calling schemas, and middleware layers that allow LLM-based agents to interact with laboratory control systems safely and reliably.
Implement state management, error recovery, and human-in-the-loop checkpoints for agent-driven experimental workflows.
Contribute to the definition of integration standards and patterns for agentic AI tool use across the Shanghai LAL site and in alignment with global Frontier AI architecture.
Collaboration: Vendors, Automation Teams & Global Tech@Lilly
Serve as the integration focal point between local automation teams, scientists, laboratory vendors, and global / local Tech@Lilly teams ensuring that digital solutions are co-defined and co-developed with shared standards and clear interfaces.
Work closely with automation engineers and instrument vendors to understand hardware capabilities, define integration requirements, and validate integration behavior in the lab environment.
Partner with global Tech@Lilly platform, data, and security teams to ensure solutions adhere to enterprise architecture standards, data governance policies, and integration best practices.
Drive vendor technical engagements, including API documentation review, integration testing, and escalation of technical issues.
Compliance, Security & Solution Deployment
Ensure all integration solutions are designed and deployed in accordance with Lilly's standards for cybersecurity, computer system validation (CSV/GxP), data privacy, and regulatory compliance.
Collaborate with IT security, compliance, and validation teams during solution design and deployment to meet audit and regulatory requirements applicable to pharmaceutical R&D environments.
Deploy and maintain containerized services using Docker and Kubernetes with CI/CD pipelines and MLOps practices.
Integrate cloud-based orchestration frameworks with laboratory control systems and scheduling engines.
Maintain observability and monitoring across integration layers using tools such as Prometheus, Grafana, or Datadog.
职位要求
What Success Looks Like
Measurable reduction in business process turnaround time through seamless, secure integration between AI reasoning, digital platforms, and lab operations.
Business requirements are consistently translated into well-documented, fit-for-purpose technical solutions that scientists and engineers adopt as indispensable parts of their workflows.
Integration solutions scale from pilot deployments at the Shanghai site to production environments globally, in alignment with Tech@Lilly standards.
Integration solutions and AI agents reliably execute and adapt experiments on physical laboratory instruments through cloud-orchestrated, validated integration pipelines.
All solutions are delivered compliant with applicable cybersecurity, CSV, and data privacy requirements, with clear audit trails.
Basic Qualifications
MS 5+ yrs / BS 7+ yrs equivalent experience in Computer Science, Computer Engineering, Chemical Engineering, Robotics, or a related discipline with demonstrated software development, system integration, or lab automation engineering experience.
Strong experience with cloud including compute, networking, messaging, and storage services.
Hands-on experience with containerization (Docker) and Kubernetes-based orchestration in production environments.
Demonstrated experience integrating software systems with laboratory automation platforms, instruments, scientific data systems (LIMS, ELN), or IoT devices.
Proficiency in Python with production-quality engineering practices: testing, CI/CD, version control.
Experience designing and building scalable APIs using frameworks such as FastAPI, Flask, or gRPC.
Familiarity with infrastructure-as-code tools (Terraform, CloudFormation) and GitOps deployment patterns.
Demonstrated business analysis skills: ability to gather, structure, and translate business and scientific requirements into technical designs and implementation plans.
Preferred Qualifications
Direct experience integrating AI or algorithmic decision systems with laboratory automation (liquid handlers, analytical instruments, robotic workflows).
Experience designing and implementing agentic AI integration architectures, including multi-agent frameworks, tool integration, and planning/execution loops.
Working knowledge of cloud-native pipeline orchestration tools such as Argo Workflows, Apache Airflow, or AWS Step Functions.
Experience with LIMS, ELN, or scientific data management systems in pharmaceutical or biotech environments.
Familiarity with LLM application development, function calling, and tool-use patterns (e.g., MCP, OpenAI Function Calling).
Experience with security, compliance, and computer system validation (CSV/GxP) frameworks for software solutions in regulated pharmaceutical or healthcare environments.
Experience with observability and monitoring stacks (Prometheus, Grafana, Datadog, or similar).
Working proficiency in Mandarin Chinese and English — strongly preferred for this Shanghai-based role.
Demonstrable research or project contributions, ideally evidenced through publications or open-source work in relevant venues.
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