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fa-ewjt-saasfaprod1-cx2

Agentic AI Data Engineer

Gurgaon · Senior

Older listing - lower visibility likelyVerified listingPosted 78d ago

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97Experience

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Skills required

27 listed
Retrieval-Augmented Generation (RAG)Large Language Models (LLMs)CI/CDAgentic AIMachine LearningAmazon S3Amazon RedshiftAmazon Bedrock+19 more
Retrieval-Augmented Generation (RAG)Large Language Models (LLMs)CI/CDAgentic AIMachine Learning

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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

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

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Two ways in

Applicants who checked fit first are 3.1× more likely to hear back

Your match scoreFree · sign in
86Overall
64Skills
97Experience

Your score for this role already exists

ASAI compared this JD against 41 signals - skills, seniority, domain, stack overlap etc. Add a resume and it unlocks in about 30 seconds.

Always free · no credit card · 1 tap with Google

Free · no signup

Get tomorrow's jobs before you have to search

Daily job drops, skill trends and free resources - posted straight to the group. Leave any time.

Join WhatsAppJoin Telegram

No spam. Just jobs and resources.

Why people use ASAI

Someone shared one job with you. ASAI keeps finding the rest.

  • Scored, not searched. Every role ranked against your actual profile.

  • Alerts as often as hourly. Reach new roles while the pile is still small.

  • Skill gaps, spelled out. See exactly which requirements you don't meet yet.

  • Verified jobs, only. Say no to ghost jobs. Your time deserves respect.

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