Modernize legacy Hadoop and Spark workflows by migrating them to AWS and Databricks, while building AI-ready data pipelines, intelligent agents, LLM applications, and RAG solutions. Maintain existing big data infrastructure, optimize Delta Lake workloads, resolve performance issues, and support BI stakeholders by troubleshooting dashboards and data extracts.
We are seeking an ambitious Data and AI Engineer with 5-8 years of experience to play a pivotal role in modernizing client’s core data infrastructure and scaling advanced AI capabilities. This role bridges legacy big data environment and future cloud platform. You will actively maintain and optimize our existing data pipelines while architecting, developing, and transitioning workflows to an AI-ready cloud ecosystem. Beyond traditional data pipelines, you will play an active role in building and deploying intelligent AI agents and leveraging advanced Large Language Models (LLMs) like Anthropic Claude, utilizing the cutting-edge Databricks AI suite to deliver immediate business value.
ResponsibilitiesCore Responsibilities
- Cloud Modernization & Migration: Deconstruct legacy Apache Spark and Hadoop MapReduce workflows to re-architect and rebuild them as optimized, production-ready pipelines within AWS and Databricks.
- AI Agent & LLM Development: Design, build, and deploy intelligent AI agents and workflow automation tools leveraging leading Large Language Models (LLMs) such as Anthropic Claude.
- AI Data Pipeline Engineering: Build and optimize pipeline architectures explicitly tailored for AI use cases, including unstructured data ingestion, real-time feature tokenization, and metadata tagging for vector databases.
- Legacy Infrastructure Maintenance: Monitor, maintain, and troubleshoot existing big data workloads running on our Hadoop cluster to guarantee data availability for business operations during the multi-phase migration, resolving bottlenecks and Out-Of-Memory (OOM) errors.
BI Engineering & Support: Act as the primary engineering liaison for downstream business stakeholders utilizing BI tools (e.g. Tableau, Looker, etc.) by performing minor functional enhancements, bug fixes, and data extract optimizations to resolve report dashboard latency.
- Cloud Optimization: Utilize Databricks and Delta Lake features (e.g., ACID transactions, Z-Ordering, caching) to significantly improve pipeline performance, reliability, and cost efficiency.
- Databricks AI Suite Implementation: Leverage Databricks tools (such as Databricks Vector Search, Mosaic AI, and Lakeflow) to orchestrate, track, and serve production-grade Generative AI and Retrieval-Augmented Generation (RAG) applications.
Required Qualifications
- Experience: 5 to 8 years of professional software engineering or data engineering experience in a production environment.
- AI & Agentic Frameworks: Hands-on experience or deep technical familiarity building functional AI agents, integrating LLM APIs (specifically Anthropic Claude), and utilizing orchestration frameworks (e.g., LangChain, Databricks Mosaic AI Agent Framework or any other tool ).
- Distributed Computing: Foundational understanding of distributed storage and computing concepts—specifically partitioning, shuffling, caching, and broadcast joins. Solid hands-on experience with Apache Spark is required.
- Programming & SQL: Strong proficiency in Python (PySpark) or Scala, alongside intermediate-to-advanced SQL querying capabilities (window functions, query tuning, and complex joins).
- Cloud & Databricks Exposure: Direct experience or deep theoretical knowledge of the AWS ecosystem (S3, IAM) and Databricks environments.
- Visualization Layer: Practical experience working with any BI tool (e.g. Tableau, Looker, Power BI, etc.) with the capability to debug calculated fields, modify parameters, and troubleshoot slow-loading reports.
- Education: Bachelor’s degree in Computer Science, Data Engineering, Information Systems, or a related quantitative field, or equivalent practical experience.
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