🔥 [Onsite HCM] Senior AI/ Data / ML Engineer

Job description:

1. AI / Machine Learning & Analytics

• Design, develop, and deploy Machine Learning and AI solutions for enterprise use cases.
• Build and optimize ML pipelines covering data preparation, feature engineering, training, evaluation, and deployment.
• Work on NLP use cases such as text classification, information extraction, semantic search, and chat-based systems (if applicable).
• Support AI model operationalization (MLOps / AIOps) in cloud environments.
• Collaborate with Data Engineers to ensure data is analytics- and ML-ready.

2. Data Engineering & Data Platforms

• Design and implement data ingestion pipelines from multiple data sources (batch and event-driven).
• Process large-scale datasets (100GB+ CSV/JSON/Parquet) using Spark / PySpark.
• Work with Azure Data Lake (ADLS Gen2) and structured/unstructured data.
• Build and optimize data models for reporting, analytics, and AI use cases.
• Implement Slowly Changing Dimensions (SCD Type 1 / Type 2) and historical data handling.

3. Cloud & Big Data Technologies

• Develop solutions on Microsoft Azure, including:
• Azure Databricks
• Azure Data Factory (ADF)
• Azure Functions
• Azure Storage (ADLS Gen1 / Gen2)
• Azure DevOps (CI/CD, pipeline triggers)
• Design and explain end-to-end data lake architectures.
• Understand and apply Databricks concepts:
• Workspace vs Cluster vs Job
• Catalog / Data governance concepts
• Cost and performance considerations

4. Programming & Software Engineering

• Strong hands-on experience with Python for data processing and ML.
• Solid understanding of SQL, including complex joins, aggregations, window functions, and partitioning.
• Experience with PySpark and Spark execution models (driver, executors, partitions).
• Perform query performance tuning, especially on PostgreSQL:
• Indexing strategies (partial indexes, expression indexes)
• Partitioning strategies
• Query plan analysis
• Collaborate with frontend developers and testers in an agile environment.
• (Optional) Fullstack capabilities for AI-driven applications (API, backend, basic UI integration).

5. Data Quality & Business Logic

• Implement data quality checks and validation rules.
• Handle inconsistent, missing, or conflicting data from multiple sources.
• Validate categorical values and business rules (e.g., Yes/No, 0/1).
• Transform raw data into reporting- and dashboard-ready datasets.
• Explain technical logic and business metrics clearly to non-technical stakeholders.

6. Professional Communication & Collaboration

• Actively communicate with clients, partners, and internal teams.
• Explain architecture, data logic, and trade-offs confidently in English.
• Participate in requirement clarification, technical discussions, and knowledge transfer.
• Work effectively with stakeholders in regulated or sensitive data environments (e.g., banking, finance, enterprise systems).

Qualification

Must-have:

• 6+ years of experience in one or more of the following:
• AI Engineering
• Machine Learning Engineering
• Data Engineering
• ML + Software Engineering

Strong experience with:

• Python
• SQL
• Spark / PySpark
• Azure Databricks
• Azure Data Factory
• Solid understanding of:
• Data modeling (normalized vs denormalized)
• Data quality management
• Cloud-based data lake architectures
• Fluent English communication (spoken & written)

Nice-to-have

• NLP experience
• Kafka / event-driven architectures
• Power BI / Power Platform exposure
• Experience working in banking, financial services, or regulated domains
• MLOps / model deployment experience

📩 Contact: Ms. Bao Han – Talent Acquisition

📱 Tel/Zalo: 0943200202