AI Engineer
At a glance
Mid-level AI Engineering role at Thermofisher. Jiangsu · full-time.
Pay not stated AI Engineer salary
aimlroles summary, based on the employer's posting.
What you'll do
- Spot useful AI applications across factory, quality, supply-chain, engineering and office work, then assess their potential
- Build and launch computer-vision inspection, OCR, forecasting, generative assistants or agent-based tools
- Turn stakeholder problems into AI, data and outcome requirements that teams can evaluate
- Prepare data, validate deployed models, track results and address issues to improve solution performance
- Connect AI workflows to MES, ERP, vision systems and production processes, then train users and support adoption
What you bring
- A bachelor's degree in computer science, AI, data science, automation, engineering or a related discipline
- Three to five years in an AI, data science, machine-learning, vision or intelligent-automation position
- Strong analytical and problem-solving skills, including drawing conclusions from data and reviewing model results
- Strong spoken and written English for collaborating in a multicultural workplace
Who this fits
This role suits an AI engineer who can work independently and with cross-functional teams, make technical decisions and resolve problems. The schedule is Monday through Friday, and the role is based in Suzhou. Occasional domestic and international travel is required. A master's degree and experience with industrial AI, Python, SQL, Power BI or AI platforms are preferred.
From the employer
Work Schedule
Standard (Mon-Fri)Environmental Conditions
OfficeJob Description
Title: AI Engineer
Organization: China Operations-Suzhou
Location: Suzhou, PRC
Reports To: MADE Leader
Position Summary:
The MADE AI Engineer is primarily responsible for developing, deploying and optimizing AI solutions that improve manufacturing, quality, supply chain and office workflow efficiency. The role includes identifying AI application opportunities, building and validating AI models or AI-agent solutions, integrating AI tools into business and shopfloor processes, and working with cross-functional teams to accelerate intelligent transformation under MADE.
Besides overall AI engineering, this role will also support AI solution governance, user enablement, prompt and workflow design, data preparation and continuous improvement of deployed AI solutions. Work as an important MADE team representative to connect operations needs with practical AI implementation.
Essential Job Functions and Accountabilities:
- Identify and evaluate AI use cases in manufacturing, quality, supply chain, engineering and office workflows.
- Design, develop and deploy AI solutions such as computer vision inspection, OCR, predictive analytics, generative AI assistants and AI agents.
- Work with stakeholders to translate business problems into AI solution requirements, data requirements and measurable outcomes.
- Support data collection, data cleaning, annotation, model evaluation and performance optimization activities.
- Develop AI workflows, prompt strategies, agent logic and integration methods to improve task automation and decision support.
- Collaborate with digitalization and automation teams to integrate AI solutions into MES, ERP, vision systems, office tools and production workflows.
- Conduct testing, validation and monitoring of deployed AI solutions to ensure reliability, accuracy and usability.
- Troubleshoot AI model, data or workflow issues and drive continuous improvement actions.
- Provide user training, technical documentation and adoption support for deployed AI tools.
- Ensure AI solutions are aligned with business objectives, compliance requirements and data governance expectations.
- The ability to perform independently and as part of a team.
- Must be able to influence and negotiate with people and resolve issues/problems.
- Communicate recommendations and decisions across the organization.
- Demonstrated ability to lead in a fast-paced technical environment, absorb technical information, review technical information and make technical decisions.
Other Job Requirements:
Occasional travel is required, including domestic and international travel.
Educational Qualifications:
- Bachelor Degree in Computer Science, Artificial Intelligence, Data Science, Automation, Engineering or related field; Master degree is preferred.
Experience Qualifications:
- 3-5 years direct experience in AI engineering, data science, machine learning, computer vision or intelligent automation role.
- Experience in biotech, pharmaceutical, manufacturing or industrial AI application is preferred.
- Direct experience in computer vision, OCR, machine learning model deployment, LLM application or AI-agent development is preferred.
- Familiarity with Python and common AI or ML frameworks and deployment workflows is preferred.
- Experience with manufacturing, quality or supply chain business scenarios is highly preferred.
- Proficient with Microsoft tools; Word, Excel, Power Point; experience with Python, SQL, Power BI or AI platforms is preferred.
- Strong analytical, problem solving and teamwork skills are required.
- Experience with structured problem solving and model performance analysis.
- Ability to analyze and process data and draw the appropriate conclusions.
- Demonstrated knowledge of AI lifecycle management, model validation and continuous improvement methodologies.
- Experience prioritizing conflicting demands.
- Excellent interpersonal, organizational, and influencing skills.
- Experience working with cross-functional teams and facilitating teams to identify and implement solutions to complex problems.
- Excellent oral and written communication skill in English, be able to work in a multicultural environment.
- Practical experience in integrating AI tools with enterprise or manufacturing systems is preferred.
- Ability to achieve goals independently within a challenging time frame.
Metrics:
- Number of AI solutions deployed and adopted.
- AI model or workflow accuracy and stability.
- Productivity or HC saving enabled by AI.
- Defect detection improvement or decision efficiency improvement.
- User adoption and business impact of AI solutions.