MES Developer

该职位来源于猎聘 A brief summary of the provided position as below:

We are looking for a software engineer with a strong foundation in computer science or a closely related field, who can design and build data-centric systems, reliable application software, and AI-enabled features. You will work with product, data, and operations teams to turn requirements into maintainable code, pipelines, and services.

To perform this job successfully, an individual must be able to perform each essential duty satisfactorily:

Design, implement, and operate data pipelines, APIs, and backend services (ingestion, transformation, quality checks, and delivery to analytics or applications).

Build and integrate machine learning or LLM-based capabilities where appropriate: feature preparation, model serving interfaces, evaluation hooks, and observability.

Collaborate on data models, schemas, and governance practices (documentation, lineage awareness, access patterns).

Write clear, tested code; participate in code reviews; improve reliability and performance of existing systems.

Partner with stakeholders to clarify data definitions, SLAs, and success metrics for production systems.

Education and/or Experience

Bachelor’s degree from university, majoring in Computer Science, Software Engineering, Information Systems.

Workable in both spoken and written English

Knowledge, Skills And Abilities Proficiency in at least one general-purpose language (e.g. Python, Java, C#, Go, or TypeScript/Node.js).

Solid understanding of algorithms, data structures, version control (Git), and CI/CD basics.

Experience building REST services, batch or streaming jobs, and working with SQL databases.

Hands-on experience with relational databases and writing efficient SQL.

Familiarity with ETL/ELT patterns, data validation, and basic data quality practices.

Exposure to at least one of: data warehouses / lakes (e.g. Snowflake, BigQuery, Databricks, Synapse), object storage, or message queues / event streams (e.g. Kafka, RabbitMQ, cloud-native equivalents).

Understanding of ML lifecycle: training vs inference, feature engineering at scale, and model evaluation concepts.

Experience with Python ML stack (e.g. scikit-learn, pandas, PyTorch or TensorFlow) or integration of LLM APIs (prompting patterns, guardrails, retrieval-augmented workflows) in application code.

Awareness of responsible AI topics: bias, privacy, and safe deployment.

Curiosity about business and operational data; ability to explain technical trade-offs to non-engineers.

Comfort operating in ambiguous problem spaces and iterating with feedback.