Agentic AI Data Engineer

Agentic AI Data Engineer Role Overview Total Experience required : 5-10 Years We are seeking a highly skilled Agentic AI Data Engineer to design, build, and optimize intelligent, autonomous data systems that power next-generation AI applications. This role blends data engineering, machine learning infrastructure, and emerging agent-based AI frameworks to enable scalable, self-orchestrating pipelines and decision-making systems. You will work at the intersection of data platforms, large language models (LLMs), and cloud-native architectures—building systems that can reason, act, and adapt autonomously. Key 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 Required Technical Skills 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 Preferred Qualifications • Experience building autonomous AI agents for enterprise use cases • Knowledge of multi-agent collaboration systems and planning algorithms • Familiarity with LangChain, LlamaIndex, or similar frameworks • Experience with MLOps and LLMOps practices • Understanding of graph-based workflows and knowledge graphs • Exposure to real-time AI systems and event-driven architectures Soft Skills • Strong problem-solving and system design skills • Ability to work in fast-paced, evolving AI environments • Effective communication and cross-functional collaboration • Curiosity and adaptability to emerging AI technologies Education & Experience • Bachelor’s or Master’s degree in Computer Science, Engineering, or related field • 4+ years of experience in data engineering or ML engineering • Hands-on experience with production-grade AI/ML systems Nice-to-Have • Experience with reinforcement learning or planning systems • Background in distributed systems design • Contributions to open-source AI/data projects • Certifications in AWS (e.g., Solutions Architect, Machine Learning Specialty) What You’ll Build • Autonomous data pipelines that self-heal and optimize • AI agents capable of reasoning over enterprise data • Scalable LLM-powered applications integrated with business workflows • Intelligent systems that move beyond automation into decision-making Key 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 Required Technical Skills 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 Preferred Qualifications • Experience building autonomous AI agents for enterprise use cases • Knowledge of multi-agent collaboration systems and planning algorithms • Familiarity with LangChain, LlamaIndex, or similar frameworks • Experience with MLOps and LLMOps practices • Understanding of graph-based workflows and knowledge graphs • Exposure to real-time AI systems and event-driven architectures