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