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

- Robust 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

📌 Agentic AI Data Engineer (Gurugram)
🏢 Recognized
📍 Gurugram