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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Agentic AI Data Engineer - **Company:** EXL SERVICE - **Location:** United States - **Experience:** Experienced - **Salary:** $150,000.0 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Amazon S3, Continuous Integration, Information Engineering, Extract Transform Load (ETL), Distributed Computing Environment, Python (Programming Language), Machine Learning, Standard Sql, Azure Machine Learning, SQL Databases, Unstructured Data, AI Infrastructure, Data Logging, Large Language Models, Multi-Agent Systems, Prompt Engineering, Apache Spark, Generative AI, Containerization, Data Lakes, Pyspark, Kubernetes, Information Technology, Apache Kafka, Machine Learning Operations, Video Streaming, Virtual Agents, Data Pipelines, Docker, Amazon Redshift, Microservices - **Published:** May 15, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=cf62a60610273c1a ## About the Role Do you have experience in SQL databases?, Cloud & AWS Ecosystem * Strong experience with AWS services, including: + Amazon S3, Glue, Lambda, Step Functions + Amazon Redshift / Athena + Amazon SageMaker (training, deployment, pipelines) + Amazon Bedrock (foundation models, agents, knowledge bases) AI/ML & Agentic Systems * Experience with LLMs and generative AI systems * Hands-on with agent frameworks (e.g., multi-agent orchestration, tool calling, planning systems) * Familiarity with AgentCore / agent orchestration platforms * Understanding of RAG architectures , embeddings, and vector databases * Experience with model deployment, inference optimization, and prompt engineering Data Engineering * Strong proficiency in Python and SQL * Experience with ETL/ELT tools and frameworks * Distributed data processing (Spark, PySpark, or similar) * Streaming technologies (Kafka, Kinesis, or similar) * Data modeling and schema design Data & AI Infrastructure * Experience with vector databases (e.g., Pinecone, FAISS, OpenSearch) * Knowledge of data lakehouse architectures (Delta Lake, Iceberg, Hudi) * Containerization (Docker) and orchestration (Kubernetes) * CI/CD for ML and data pipelines, Cloud & AWS Ecosystem * Strong experience with AWS services, including: + Amazon S3, Glue, Lambda, Step Functions + Amazon Redshift / Athena + Amazon SageMaker (training, deployment, pipelines) + Amazon Bedrock (foundation models, agents, knowledge bases) AI/ML & Agentic Systems * Experience with LLMs and generative AI systems * Hands-on with agent frameworks (e.g., multi-agent orchestration, tool calling, planning systems) * Familiarity with AgentCore / agent orchestration platforms * Understanding of RAG architectures , embeddings, and vector databases * Experience with model deployment, inference optimization, and prompt engineering Data Engineering * Strong proficiency in Python and SQL * Experience with ETL/ELT tools and frameworks * Distributed data processing (Spark, PySpark, or similar) * Streaming technologies (Kafka, Kinesis, or similar) * Data modeling and schema design Data & AI Infrastructure * Experience with vector databases (e.g., Pinecone, FAISS, OpenSearch) * Knowledge of data lakehouse architectures (Delta Lake, Iceberg, Hudi) * Containerization (Docker) and orchestration (Kubernetes) * CI/CD for ML and data pipelines * Qualifications: Bachelor's or Master's degree in Computer Science, Engineering, or related field * 4+ years of experience in data engineering or ML engineeringHands-on experience with production-grade AI/ML systems ## Description * Design and implement agentic AI systems that autonomously orchestrate data workflows and decision pipelines * Build scalable data pipelines for structured and unstructured data (batch + real-time) * Develop and manage LLM-powered applications using retrieval-augmented generation (RAG), tool use, and multi-agent frameworks * Integrate AWS AI/ML services into production-grade architectures * Develop and optimize data lakes, warehouses, and lakehouse architectures * Build APIs and microservices to expose AI/ML capabilities * Ensure data quality, governance, and security across pipelines * Collaborate with data scientists, ML engineers, and product teams to deploy AI solutions * Implement monitoring, logging, and observability for AI agents and pipelinesOptimize cost and performance of cloud-based AI workloads ## Related Videos - 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