เงื่อนไขการค้นหา

ประสบการณ์:

3 ปีขึ้นไป

ทักษะ:

Postgre SQL, MongoDB, Oracle

ประเภทงาน:

งานประจำ

• Install, configure, and maintain MongoDB, Oracle, SQL Server, and other relational databases.
• Ensure database performance, availability, scalability, and security across multiple platforms.
• Design and implement backup, recovery, disaster recovery, and high availability solutions.
• Manage Replication, Sharding (for MongoDB), Clustering (for Oracle/SQL Server) and performance tuning.
• Administer user accounts, roles, and access control policies.
• Proactively monitor and troubleshoot database performance issues.
• Collaborate with application developers to optimize queries, indexing, and schema design.
• Document standard operating procedures, best practices, and system configurations.
• Bachelor's degree in Computer Science, Information Technology, or related fields.
• 3+ years of experience in database administration, covering both MongoDB or RDBMS (Oracle, SQL Server, MySQL, PostgreSQL).
• Experience in backup/restore strategies, replication, clustering, and disaster recovery planning.
• Proficiency with Linux/Unix and scripting (Shell, Python, or PowerShell).
• Hands-on experience with monitoring tools such as Prometheus, Grafana, OEM, or equivalent.
• Solid understanding of security and compliance standards in database management.

ประสบการณ์:

3 ปีขึ้นไป

ทักษะ:

Business Development, Architecture, Postgre SQL, Leadership Skill, Big Data

ประเภทงาน:

งานประจำ

• Data management and governance.
• Analytics and reporting platforms.
• AI and machine learning readiness.
• Modern cloud data architecture.
• This role spans consulting, solution architecture, technical delivery, customer engagement, and continuous platform improvement.
• Depending on experience, the role may also include leading technical teams, mentoring engineers, and supporting business development initiatives.
• Business Consulting & Customer Engagement.
• Engage with clients to understand business objectives and data challenges.
• Conduct workshops, discovery sessions, and assessments.
• Translate business requirements into scalable technical solutions.
• Support Sales and Presales activities.
• Contribute to proposals, SOWs, roadmaps, and estimations.
• Data Platform & Solution Architecture.
• Design enterprise-scale Data Platform architectures.
• Develop High-Level Design (HLD) and Low-Level Design (LLD).
• Data Warehouse.
• Data Lake / Lakehouse.
• Analytics Platforms.
• Data Governance Platforms.
• Streaming Data Platforms.
• AI-ready Data Foundations.
• Define data integration, storage, governance, security, and lifecycle strategies.
• Recommend modernization and cloud adoption strategies.
• Data Engineering & Platform Delivery.
• Implement and deliver Data Platform solutions.
• Support data ingestion, transformation, and integration processes.
• Participate in migration and modernization projects.
• Ensure scalability, reliability, performance, and operational readiness.
• Support deployment, testing, and handover to operations.
• Data Governance & Data Management.
• Support implementation of Data Governance frameworks.
• Implement Data Quality, Metadata Management, Data Catalog, and Data Lineage solutions.
• Work with OpenMetadata for metadata-driven governance and cataloging.
• Support Master Data Management (MDM) initiatives.
• Ensure compliance with data management standards and regulations.
• Data Integration & Streaming.
• Design and support ETL/ELT and CDC pipelines.
• Support batch and real-time data processing architectures.
• Contribute to event-driven and streaming solutions.
• Platform Monitoring, Security & Optimization.
• Implement monitoring and observability practices.
• Support performance tuning and capacity planning.
• Ensure security, access control, and compliance requirements.
• Improve reliability and operational efficiency of platforms.
• Technical Leadership.
• Provide technical guidance and mentoring to team members.
• Lead technical workstreams and delivery execution.
• Support project planning and governance activities.
• Collaborate with Sales and Presales teams on solution design.
• Act as a trusted advisor for Data Platform strategy and modernization.
• Define standards and reusable architecture frameworks.
• Support internal knowledge sharing and capability development.
• Education.
• Bachelor's degree in Computer Science, Engineering, Information Technology, Data Science, or related field.
• Experience.
• 5+ years of experience in Data Platform, Data Engineering, DBA, Data Architecture, or Data Governance.
• Experience delivering enterprise data projects and customer-facing engagements.
• Experience in consulting, system integrator, or professional services environments is preferred.
• Technical Skills.
• Database: RDBMS, performance tuning, HA, backup & recovery, DR, migration.
• (Oracle, SQL Server, PostgreSQL, MySQL, Azure SQL, AWS RDS).
• Data Platforms: Data Warehouse, Data Lake, Lakehouse, data modeling & pipelines.
• (Snowflake, Databricks, BigQuery, Redshift, Synapse, Microsoft Fabric).
• Data Integration: ETL/ELT, CDC, data ingestion, batch & real-time processing.
• (Kafka, Airflow, dbt, SSIS, Informatica, Talend).
• Big Data: distributed processing & large-scale data workloads.
• (Spark, Flink, Hadoop, Databricks).
• Data Governance: data quality, metadata, catalog, lineage, MDM, compliance.
• (Collibra, Alation, Informatica, OpenMetadata).
• Cloud: cloud data platforms and hybrid/multi-cloud architecture.
• (AWS, Azure, GCP, OCI).
• AI & Analytics (Optional): AI-ready data architecture, ML/GenAI support.
• (Azure AI Search, OpenSearch, Pinecone, Weaviate, pgvector).
• Monitoring: observability, performance, security, operations.
• (Datadog, Dynatrace, Prometheus, Grafana, Splunk, Zabbix).
• Soft Skills.
• Strong analytical and problem-solving abilities.
• Excellent communication and stakeholder management skills.
• Customer-facing consulting mindset.
• Strong leadership and mentoring capabilities.
• Business-oriented thinking.
• Ability to work independently and in teams.
• Strong English communication skills.
• Preferred Qualifications.
• Experience in Data Modernization or Cloud Transformation programs.
• Exposure to AI/ML or Advanced Analytics initiatives.
• Experience in consulting, SI, or managed services environments.
• Cloud or Data Platform certifications.
• Experience across enterprise industries (Banking, Telco, Retail, Manufacturing, etc.).

ประสบการณ์:

5 ปีขึ้นไป

ทักษะ:

Architecture, Postgre SQL, Spring Boot, MySQL, English

ประเภทงาน:

งานประจำ

• Provide 24-hour response to operational failures of the bank's core banking system, covering business anomalies in application systems (e.g., deposit, loan, report, and document systems) and infrastructure anomalies (e.g., cloud services, databases, and networks);.
• Conduct real-time monitoring of system issues and respond promptly; escalate issues to the subsequent handling process in a timely manner, and assist in extracting logs and data required for issue analysis;.
• Upon receiving the bank's MA support request, initiate the response process within 15 minutes, and quickly complete preliminary localization through troubleshooting of abnormal phenomena, log analysis, and data verification;.
• Cooperate efficiently with the bank to resolve Serv1 or Serv2-level system issues;.
• Prepare detailed incident reports, and accurately communicate the results of root cause analysis to the bank and support teams;.
• Organize the MA processes of the core system, iteratively update the MA support manual, and ensure the implementation of standardized MA practices.
• A bachelor's degree or above, majoring in computer science, software engineering, or related fields.
• Have in-depth familiarity with the application architecture of the bank's core systems, master relevant knowledge of cloud environment operation and maintenance, and possess professional knowledge of IT Service Management (ITSM) best practices.
• Have excellent written and verbal communication skills, and be able to convey complex information clearly and concisely. Thai or English communication skills are required (candidates who can speak Chinese will be given priority).
• Recognize and accept the 24-hour, 7-day shift system, and be able to adapt to night shifts and holiday duty (each shift includes 30-minute shift-handover time).
• Have strong coordination and organizational abilities, and be able to manage multiple tasks efficiently and prioritize them clearly.
• Be familiar with the Spring Boot framework and have Java coding and development capabilities.
• Be familiar with the access operations of mainstream databases (MySQL, PostgreSQL), and have the ability to optimize SQL, tune performance, and optimize batch tasks.
• Be proficient in mainstream development tools such as Eclipse and IntelliJ IDEA, be skilled in using version management tools such as Git and SVN, and log management and analysis tools such as Kibana.
• Be able to work stably in Thailand for a long time, have good self-learning ability, and be able to quickly adapt to business and technological updates.

ประสบการณ์:

3 ปีขึ้นไป

ทักษะ:

Product Development, Postgre SQL, Automation, Javascript, Kubernetes

ประเภทงาน:

งานประจำ

• Design, develop, and fine-tune Generative AI solutions using leading LLMs such as GPT-4o, Claude (Anthropic), Gemini, and open-source models (LLaMA, Mistral, etc.).
• Build and maintain End-to-End AI Chatbot systems - from prompt engineering and LLM integration to deployment and monitoring.
• Develop production-grade RAG (Retrieval-Augmented Generation) Chatbot pipelines integrating vector search, document chunking, embedding models, and LLM inference.
• Implement and manage Vector Storage solutions using FAISS, Qdrant, Weaviate, Pinecone, or similar technologies for semantic search and knowledge retrieval.
• Design and deploy Agentic AI systems using frameworks such as LangGraph, AutoGen, CrewAI, or custom multi-agent orchestration workflows.
• Integrate AI capabilities with enterprise APIs, databases, and internal platforms via LangChain, LlamaIndex, or equivalent tooling.
• Stay current with rapidly evolving AI/ML tools including Hugging Face, Ollama, vLLM, OpenAI APIs, Vertex AI, AWS Bedrock, and Azure OpenAI Service.
• AI Product Development & MLOps.
• Own the end-to-end AI product lifecycle: requirements gathering, model selection, development, testing, deployment, and continuous improvement.
• Build and manage scalable AI inference pipelines with performance monitoring, logging, and cost optimization.
• Design prompt engineering strategies, evaluation frameworks, and guardrails for safe and reliable LLM outputs.
• Implement CI/CD workflows for AI models using MLflow, DVC, or similar MLOps tools.
• Collaborate with Data Science and Engineering teams to productionize machine learning and AI models.
• Identify opportunities to embed AI capabilities into business workflows, automation, and decision-making processes.
• Data Engineering & Platform (Foundational Awareness).
• Understand the fundamentals of data pipeline concepts (ETL/ELT, batch and streaming) to effectively collaborate with Data Engineering teams.
• Able to read, query, and work with data from common platforms such as Databricks, Snowflake, BigQuery, or PostgreSQL.
• Understand data flow from source systems to data warehouses/lakehouses to inform AI model inputs and outputs.
• Familiar with vector database concepts and embedding pipelines sufficient to configure and use them in AI projects.
• Capable of communicating data requirements clearly to Data Engineers when building AI features or knowledge bases.
• Collaboration & Innovation.
• Partner with product managers, business stakeholders, and engineers to translate business requirements into AI solutions.
• Contribute to and promote best practices for AI development, testing, documentation, and responsible AI usage.
• Mentor junior engineers and share knowledge on emerging AI tools, frameworks, and research.
• Evaluate and adopt new AI/ML technologies and frameworks to keep the team at the forefront of innovation.
• Effectively manage multiple priorities and deliver high-quality solutions independently and as part of a team.
• Bachelor's degree or higher in Computer Science, Computer Engineering, Information Technology, Artificial Intelligence, or a related field.
• 3+ years of hands-on experience in AI/ML engineering with a focus on Generative AI and NLP applications.
• Proven experience building Generative AI solutions using GPT-4o, Claude (Anthropic), Gemini, or equivalent LLMs - this is a must.
• Hands-on experience with RAG Chatbot development: embedding pipelines, chunking strategies, retrieval tuning, and LLM response generation.
• Proficiency in Vector Storage technologies such as FAISS, Qdrant, Weaviate, Pinecone, or ChromaDB.
• Experience building End-to-End Chatbot systems including intent handling, context management, multi-turn dialogue, and API integration.
• Practical knowledge of Agentic AI frameworks (LangGraph, AutoGen, CrewAI, or similar) for building multi-step, tool-using AI agents.
• Strong Python programming skills - Python (required), SQL, JavaScript/TypeScript (a plus).
• Familiarity with popular AI/ML tools and platforms: LangChain, LlamaIndex, Hugging Face, Ollama, vLLM, OpenAI SDK, Vertex AI, AWS Bedrock, or Azure OpenAI.
• Experience with prompt engineering techniques including few-shot prompting, chain-of-thought, structured output, and function calling.
• Ability to evaluate LLM outputs using metrics and frameworks (RAGAS, TruLens, or custom eval pipelines).
• AI Frameworks: LangChain, LlamaIndex, LangGraph, AutoGen, CrewAI, Hugging Face Transformers.
• LLM Platforms: OpenAI (GPT-4o), Anthropic Claude, Google Gemini, Meta LLaMA, Mistral.
• Vector Databases: FAISS, Qdrant, Weaviate, Pinecone, ChromaDB.
• Cloud & Data: AWS / GCP / Azure AI services, Databricks, Snowflake, PostgreSQL.
• MLOps: MLflow, DVC, Docker, Kubernetes (a plus).
• Big Data: Apache Spark, Kafka (a plus).
• Experience in Retail or E-Commerce AI applications (e.g., recommendation engines, pricing AI, conversational commerce).
• Familiarity with fine-tuning and PEFT techniques (LoRA, QLoRA) for LLMs.
• Knowledge in machine/statistical learning, computer vision, or time series forecasting.
• Experience with AI safety, hallucination mitigation, and responsible AI practices.
• Contributions to open-source AI projects or published AI research.

ประสบการณ์:

6 ปีขึ้นไป

ทักษะ:

Cloud Computing, Architecture, Postgre SQL, Automation, Leadership Skill

ประเภทงาน:

งานประจำ

• AI & Advanced Analytics Enablement.
• Lead the design, development, and deployment of enterprise AI, Machine Learning, and Generative AI solutions to support business transformation (e.g., pricing, promotion, automation, recommendation, demand forecasting, customer insights, and intelligent decision support).
• Build and manage scalable data pipelines supporting AI/ML model training, inference, feature engineering, Feature Store, RAG knowledge bases, and LLM applications.
• Develop reusable datasets, AI-ready data products, feature engineering pipelines, and semantic data models for AI Engineers, Data Scientists, and Business Analytics teams.
• Partner closely with AI Engineers to design, deploy, productionize, and scale AI/ML models, LLM applications, Agentic AI, AI Chatbots, Recommendation Systems, and Mobile AI applications.
• Design and maintain data ingestion pipelines for structured, semi-structured, and unstructured data from enterprise systems, APIs, databases, files, event streams, IoT devices, and third-party platforms.
• Develop scalable data pipelines supporting document ingestion, embedding generation, metadata management, vector indexing, and retrieval workflows for RAG applications.
• Identify opportunities to embed AI into business workflows and operational decision-making to improve efficiency, customer experience, and business value.
• Data Platform & Engineering Leadership.
• Own end-to-end enterprise data architecture from source systems to Data Lake, Lakehouse, Data Warehouse, Feature Store, Semantic Layer, and AI-serving layers.
• Design, develop, and optimize scalable ETL/ELT pipelines supporting batch, micro-batch, streaming, and near real-time data processing.
• Design and maintain workflow orchestration for enterprise data pipelines using Databricks Workflows, Apache Airflow, or equivalent orchestration frameworks.
• Develop enterprise-scale Big Data solutions using Apache Spark and distributed computing frameworks.
• Design scalable logical and physical data models while optimizing database architecture for performance, scalability, reliability, and cost efficiency.
• Ensure enterprise data quality, governance, lineage, metadata management, observability, security, and compliance across data platforms.
• Implement automated data validation, monitoring, logging, alerting, and observability to ensure production-grade data reliability.
• Optimize SQL queries, Spark workloads, partitioning strategies, storage formats, and compute resources for maximum performance and cost efficiency.
• Analyze complex technical issues, identify root causes, troubleshoot production problems, and recommend infrastructure and platform improvements.
• Select, evaluate, and integrate modern data engineering tools, cloud technologies, and AI platform frameworks to support evolving business needs.
• Continuously evaluate emerging technologies in Big Data, Lakehouse Architecture, Data Platform Engineering, Cloud Computing, and AI Platform Engineering.
• AI Platform & Infrastructure.
• Build and maintain enterprise AI data infrastructure supporting LLM, RAG, Agentic AI, AI Chatbots, Recommendation Engines, Intelligent Search, and Intelligent Automation platforms.
• Design and implement scalable AI data pipelines supporting batch, streaming, vector search, embedding pipelines, and Retrieval-Augmented Generation (RAG) architectures.
• Design scalable APIs, data services, and integration layers connecting AI applications with enterprise systems and digital platforms.
• Collaborate with AI Engineers to prepare high-quality datasets for LLM fine-tuning, prompt engineering, model evaluation, inference, and continuous model improvement.
• Support deployment and operationalization of AI products using DataOps, MLOps, CI/CD, containerization, and modern software engineering practices.
• Drive continuous improvements in platform scalability, availability, security, observability, resilience, and operational efficiency.
• Team Leadership & Capability Building.
• Lead, mentor, and develop a high-performing team of Data Engineers, AI Engineers, and Analytics professionals.
• Foster engineering excellence through best practices in architecture design, coding standards, testing, code reviews, deployment, technical documentation, and software engineering.
• Drive architecture reviews, technical design reviews, engineering governance, and platform standardization across the engineering organization.
• Define engineering standards, technical roadmaps, platform architecture, and technology strategy aligned with business objectives.
• Collaborate closely with Product Owners, Business Stakeholders, AI Engineers, Data Scientists, Solution Architects, Infrastructure, and DevOps teams.
• Provide hands-on technical leadership with a strong engineering mindset and willingness to troubleshoot complex production systems.
• Promote continuous learning, knowledge sharing, innovation, and adoption of emerging technologies across the engineering team.
• Bachelor's degree or higher in Computer Science, Computer Engineering, Information Technology, Artificial Intelligence, Data Engineering, Management Information Systems, or a related field.
• 6+ years of experience in Data Platform Engineering, Data Engineering, Big Data, or AI Platform development, with experience leading engineering teams.
• Strong experience designing and implementing enterprise-scale Data Lake, Lakehouse, Data Warehouse, or Modern Data Platform architectures.
• Expert proficiency in SQL, Databricks SQL, PostgreSQL, database design, and query performance optimization.
• Strong programming skills in Python.
• Hands-on experience with Apache Spark, Databricks, Delta Lake, Spark SQL, Unity Catalog, and distributed data processing.
• Strong experience designing and developing scalable ETL/ELT pipelines and workflow orchestration.
• Experience with streaming technologies such as Spark Structured Streaming, Kafka, or equivalent.
• Experience building enterprise-scale data platforms supporting AI/ML, Advanced Analytics, and Generative AI applications.

ประสบการณ์:

3 ปีขึ้นไป

ทักษะ:

Software Development, Windows Server, Automation, Python, Oracle, Apache, VMware, Linux, SQL, GIS

ประเภทงาน:

งานประจำ

• Act as the single point of contact (SPOC) for back-office users, handling incoming tickets via ITSM and calls through the central App Support Hotline [Ext. 7777].
• Monitor application health, alert dashboards, and server resources; perform initial troubleshooting, and execute standard remediation according to SOPs.
• Log and document all issues in the ITSM system with high accuracy, ensuring proper categorization and routing to support >90% triage precision.
• Escalate unresolved or critical incidents (P1/P2) immediately to the Software Development Team Leader, SRE, or Server teams using the defined escalation protocols.
• Assist in testing application updates, system patches, and rollback procedures under the change management guidelines.
• Create and maintain technical runbooks, troubleshooting guides, and knowledge base articles, utilizing AI.
• Bachelor's degree in Computer Science, Computer Engineering, Information Technology, or a related field.
• 1-3 years of experience in Application Support, IT Helpdesk, Technical Support, or Operations roles. (Highly motivated fresh graduates are also welcome to apply).
• Familiarity with ITSM ticketing tools (preferably ManageEngine or Jira Service Desk).
• Ability to work under pressure, manage multi-tasking demands, and resolve user issues with a strong customer-service mindset.
• Strong problem-solving, analytical, and logical-thinking skills.
• Good team contributor who communicates technical issues clearly and collaborates effectively with cross-functional teams.
• Specific knowledge and skill / ความรู้เฉพาะตำแหน่ง.
• Basic Database & Queries: Ability to write and run basic SQL queries (MS-SQL, Postgre, or Oracle) for investigation and data extraction.
• Infrastructure Basics: Basic knowledge of Windows Server, Active Directory, Linux CentOS/RedHat, and virtualization (VMware).
• System Monitoring & Transfer: Familiarity with file transfer protocols (SFTP/FTP), GoAnywhere, Apache Airflow and system monitoring tools (metrics, logs, alerts).
• Financial Business Flow: Basic understanding of securities trading lifecycle, mutual funds, or back-office batch processing.
• Automation Basics: Basic scripting skills (Python, PowerShell, or Bash) is a strong advantage.
• Apply now ".

ประสบการณ์:

5 ปีขึ้นไป

ทักษะ:

Software Development, Automation, Oracle, Apache, VMware, Linux, SQL, GIS

ประเภทงาน:

งานประจำ

• Lead, mentor, and manage the Application Support squad (3 Junior engineers), ensuring high morale, optimal performance, and continuous skill development.
• Design and maintain the Shift Roster (Onshore/Offshore Day Shifts and Night Batch Specialist Shifts) with clear daily handover checklists to guarantee 24/7 coverage.
• Establish and govern clear Service Demarcation Lines and handoff points between App Support, Helpdesk, Software Development,SRE and Server teams, eliminating duplicate efforts.
• Manage the implementation and optimization of ITSM, ensuring robust processes for Incident, Problem, Change and Service Request modules.
• Partner with the Bank's IT team to implement API-level Ticket Bridging maintaining a target ticket assignment accuracy of >90%.
• Serve as the primary escalation point for application issues; cooperate with Incident Manager and Technical Teams in the "War Room" during P1/P2 incidents to rapidly restore systems and achieve SLA targets (Downtime 90%.
• Serve as the primary escalation point for application issues; cooperate with Incident Manager and Technical Teams in the "War Room" during P1/P2 incidents to rapidly restore systems and achieve SLA targets (Downtime < 4.32 mins/month).
• Specific knowledge and skill / ความรู้เฉพาะตำแหน่ง.
• System & Application Knowledge: Familiarity with core financial systems (CIS, ESS, SBL, MFET, GIS, SAXO, Front IFIS).
• Tech Stack Literacy: Good understanding of Windows/Linux servers, Active Directory, VMware virtualization, SQL databases (MSSQL, Oracle and Postgre), SFTP protocols, GoAnywhere, Apache Aitflow,.
• Core ITSM Administration: ITSM configuration, ticket routing rules, and API integrations.
• Operations & Batch Control: Knowledge of Batch scheduling, automation tools, and transaction logs processing.
• Analytical Skills: Root Cause Analysis (RCA) and Incident Trend analysis.
• Apply now ".