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