English: Fluent

Location: Remote

Term: 6 months (extendable based on performance and project needs) Working hours: GMT+7

About the role

Our client is looking for a Senior Data Engineer with strong AI/ML and data platform expertise to help build the next generation of intelligent, chatbot-driven data products. This role is a handson tech lead position with high autonomy, offering the opportunity to design end-to-end solutions that combine large language models, conversational AI, and modern cloud data pipelines. You will take ownership of developing, fine-tuning and deploying AI/ML models for chatbot applications, while designing scalable data pipelines on AWS and Databricks. You will work at the intersection of data engineering, machine learning, and product engineering — collaborating closely with frontend (React), backend (Node.js), ML, and product teams to deliver robust AI services from model to UI.

As an early member of a fast-growing team, you are expected to help shape technical direction, promote a culture of ownership, curiosity, transparency, and strong writing, and mentor more junior engineers as the team scales.

Key Responsibilities

• Develop, fine-tune, and evaluate AI/ML models for chatbot applications, with a focus on NLP and Large Language Models (LLMs).
• Build intelligent conversational flows using Databricks Genie, Databricks Multiagent, and Amazon Lex.
• Design, implement, and optimize data pipelines leveraging AWS Glue, AWS Lambda, AWS Step Functions, and Databricks.
• Own the end-to-end data platform stack, including analytical databases, orchestration layer, dashboards, and automations.
• Develop and maintain data warehouses and complementary storage systems to ensure high-quality, reliable data.
• Implement data quality checks, monitoring, and alerting systems to keep production pipelines healthy.
• Collaborate with frontend (React) and backend (Node.js) teams to integrate AI services into customer-facing products.
• Monitor, debug, and continuously improve model performance and production pipeline reliability.
• Support deployment, scaling, and troubleshooting of services running on Amazon EKS.
• Work on internal AI automations and agent-based workflows to increase team productivity.
• Lead and mentor a small team of data/ML engineers, and stay current with the latest data engineering and AI technologies.

Requirements

• More than 4 years of experience in data engineering, ML engineering, or software engineering roles in technology and data-centric businesses.
• Strong hands-on experience with Python (primary language) and SQL.
• Proven expertise in machine learning and NLP/LLMs, with track record of shipping models to production.
• Hands-on experience with cloud-based data pipelines, preferably on AWS (Glue, Lambda, Step Functions, EKS).
• Familiarity with chatbot frameworks such as Amazon Lex (or similar) and multi-agent systems.
• Experience with the Databricks ecosystem (Genie, Multiagent, Delta, etc.) is a strong plus.
• Basic understanding of backend (Node.js APIs) and frontend integration patterns.
• Working knowledge of Docker and Kubernetes, and experience deploying services to production.
• Excellent problem-solving and analytical skills; able to plan and execute tasks independently.
• Strong communicator who enjoys collaborating across functions to understand stakeholder needs.
• Thrives in a fast-paced environment where ownership, curiosity, and transparency are valued.

Nice to Have

• Experience with real-time / streaming data pipelines (e.g., Kinesis, Kafka, Spark Structured Streaming).
• Understanding of CI/CD and MLOps practices (model versioning, deployment, monitoring).
• Ability to troubleshoot full-stack issues across the AI → API → UI layers.
• Familiarity with MongoDB or other non-relational databases.
• Experience with modern data warehouses such as Snowflake, BigQuery, or Databricks SQL.
• Exposure to building AI agents and LLM-powered automations.