Note: The job is a remote job and is open to candidates in USA. EXL is a leading data analytics and digital operations company that partners with clients to reinvent business models using data and AI. The Agentic AI Data Engineer role involves designing and building intelligent data systems that enhance AI applications, focusing on data engineering, machine learning infrastructure, and agent-based AI frameworks.

Responsibilities

• 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 pipelines
• Optimize cost and performance of cloud-based AI workloads

Skills

• Experience (In Years): 6-9
• 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)
• 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
• 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
• 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

Company Overview

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