Role Overview
We are seeking an experienced Data / AI Engineer to join our client's product team, working on an enterprise AI assistant platform for a global company in the medtech/healthcare industry. This is a replacement role for an outgoing team member and is critical to maintaining operational continuity while driving forward AI innovation across the programme.
The platform is built on a dual-cloud architecture: Databricks (on Azure) for data engineering and Genie-powered analytics, and AWS Lex for the conversational chatbot layer. The new hire will maintain and strengthen this live system while contributing to agentic AI extensions and proof-of-concept initiatives.
Key Responsibilities
- Operational Maintenance (~30-50% of time)
- Maintain and support the existing chatbot infrastructure across Databricks (Azure) and AWS Lex.
- Manage data pipelines, Unity Catalog, and Delta Lake tables within Databricks Workspace.
- Ensure the integration between the Azure-hosted Databricks layer and the AWS Lex chatbot remains stable and performant.
- Monitor deployments via CloudWatch and MLflow; troubleshoot issues as they arise.
- Collaborate with the web/mobile engineering team on deployment handoffs, where the data/AI layer interfaces with the application layer.
- AI Innovation and Exploration (~50-70% of time)
- Build and improve agentic AI workflows and LLM orchestration patterns on the platform.
- Implement and iterate on Retrieval-Augmented Generation (RAG) pipelines to improve answer consistency and reduce hallucinations.
- Leverage models available via Azure AI Foundry (Claude, OpenAI) and AWS Bedrock to extend the platform's capabilities.
- Design and deliver quick proofs-of-concept (POCs) on new AI capabilities — e.g., PDF ingestion, document Q&A, richer conversational context.
- Apply prompt engineering best practices to improve response quality and reliability across the agentic framework.
- Evaluate LLM outputs systematically; contribute to model evaluation practices and trust-building within the enterprise.
- Stay current with emerging AI tooling and propose pragmatic adoption paths that align with enterprise architecture governance.
Technical Skills Required
Category | Skills / Tools |
AI / GenAI | Large Language Models (OpenAI, Claude, Llama), Prompt Engineering, Retrieval-Augmented Generation (RAG), AI Agents / Agentic Frameworks, LLM Orchestration, Model Evaluation |
Databricks | Databricks Workspace, Genie, Delta Lake, Unity Catalog, MLflow |
AWS | AWS Lex, S3, IAM, Lambda, CloudWatch, Bedrock (preferred) |
Azure | Azure AI Foundry, Azure OpenAI Service, Azure Machine Learning, Azure Storage, Azure Key Vault |
Programming | Python (primary), SQL, PySpark |
DevOps / MLOps | GitLab, Git, CI/CD Pipelines, MLOps concepts |
Machine Learning | Supervised / Unsupervised Learning (working knowledge) — Classification, Regression, Clustering, Feature Engineering; Model Evaluation |
Note: Deep ML / classical data science expertise is not the primary focus of this role. The team has separate data engineering support. The ideal candidate is AI-first, with Databricks proficiency and genuine curiosity for GenAI and agentic systems.
What We're Looking For
- Strong hands-on experience with Databricks and at least one of AWS or Azure cloud environments.
- Demonstrated knowledge of LLM-based application development — RAG pipelines, prompt design, agent orchestration.
- A practical, exploratory mindset: comfortable picking up new tools, proposing approaches, and building fast POCs.
- Experience working within enterprise architecture constraints — understanding that new tech requires governance approval.
- Strong Python skills; PySpark and SQL for data work.
- Good written and verbal English communication — this role interacts directly with a Singapore-based technology lead.
Nice to Have
- Familiarity with Databricks Genie and conversational BI patterns.
- Experience with Azure AI Foundry or Azure OpenAI Service.
- Exposure to AWS Lex or similar conversational AI platforms.
- Prior work in healthcare, medtech, or other regulated industries.
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