Agentic AI Data Engineer

Job description

**Job Description: 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 **Responsibilities: 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 **Qualifications: 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