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Day 83
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Agentic AI Data Engineer
Reads like a ML Engineer role: putting models into production.
Gurugram, Haryana, India
Live, first seen by us 83 days ago- Pay
- Pay not listed
- Work setting
Hybrid — work setting source
Work setup in the employer’s posting: Hybrid
From the employer’s posting- Employment
Full-time — employment source
Employment type in the employer’s posting: Full time
From the employer’s posting
What you’ll work on
Full postingWe are seeking a highly skilled Agentic AI Data Engineer to design, build, and optimize intelligent, autonomous data systems that power next-generation AI applications.
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.
Design and implement agentic AI systems that orchestrate data workflows
Build scalable batch and real-time pipelines for structured and unstructured data
Develop LLM applications using RAG, tool use, and multi-agent frameworks
Integrate AWS AI/ML services into production architectures
Implement monitoring, logging, and observability for AI agents and pipelines
From the employer’s posting
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.
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
Implement monitoring, logging, and observability for AI agents and pipelines
What you’ll bring
All qualificationsCore experience
- 5-10 years total experience; 4+ years in data or ML engineering
- Strong Python and SQL skills
- Hands-on AWS experience (S3, Glue, Lambda, SageMaker, Bedrock)
- Experience with LLMs and generative AI systems
- Distributed processing (Spark or PySpark) and streaming (Kafka or Kinesis)
Preferred experience
- Experience building autonomous AI agents for enterprise use cases
- Ability to work in fast-paced, evolving AI environments
- Experience building autonomous AI agents for enterprise use cases
- Knowledge of multi-agent collaboration systems and planning algorithms
Qualification wording
Total Experience required : 5-10 Years
Strong proficiency in Python and SQL
Strong experience with AWS services, including:
Experience with LLMs and generative AI systems
Distributed data processing (Spark, PySpark, or similar)
Experience building autonomous AI agents for enterprise use cases
Ability to work in fast-paced, evolving AI environments
Knowledge of multi-agent collaboration systems and planning algorithms
Education & alternatives
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
Posting record
Report an error- Status in our records
- Live
- First seen by us
- Aug 12, 202683 days ago
- Recorded sightings
- 337
- Last seen by us
- Nov 4, 2026
- Employer says posted
- Jul 30, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
How this job compares
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See how this role fits your experience
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- Python
- SQL
- AWS
- Delta Lake
- Docker
- Kafka
- Kubernetes
- Redshift
- S3
- SageMaker
- Spark
- PySpark
Where the posting names them
Data Engineering - Strong proficiency in Python and SQL - Experience with ETL/ELT tools and frameworks
- 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
Required Technical Skills Cloud & AWS Ecosystem - Strong experience with AWS services, including:
Cloud & AWS Ecosystem - Strong experience with AWS services, including: - Amazon S3, Glue, Lambda, Step Functions
- Contributions to open-source AI/data projects - Certifications in AWS (e.g., Solutions Architect, Machine Learning Specialty) What You’ll Build
- Experience with vector databases (e.g., Pinecone, FAISS, OpenSearch) - Knowledge of data lakehouse architectures (Delta Lake, Iceberg, Hudi) - Containerization (Docker) and orchestration (Kubernetes)
- Knowledge of data lakehouse architectures (Delta Lake, Iceberg, Hudi) - Containerization (Docker) and orchestration (Kubernetes) - CI/CD for ML and data pipelines
- Distributed data processing (Spark, PySpark, or similar) - Streaming technologies (Kafka, Kinesis, or similar) - Data modeling and schema design
- Amazon S3, Glue, Lambda, Step Functions - Amazon Redshift / Athena - Amazon SageMaker (training, deployment, pipelines)
- Strong experience with AWS services, including: - Amazon S3, Glue, Lambda, Step Functions - Amazon Redshift / Athena
- Amazon Redshift / Athena - Amazon SageMaker (training, deployment, pipelines) - Amazon Bedrock (foundation models, agents, knowledge bases)
- Experience with ETL/ELT tools and frameworks - Distributed data processing (Spark, PySpark, or similar) - Streaming technologies (Kafka, Kinesis, or similar)
Your next step
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About the role
We file this job under ML Engineer, by the work the posting describes.
ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.
Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.
A data scientist often works out which model to build; an ML engineer makes it run reliably in production.
US government figures for computer and information research scientists
- Median yearly pay, May 2025
- $140,300
- Expected change, 2025 to 2035
- 21.8% more jobsagainst 3.5% for all jobs
The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.
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
Source details
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- Pay
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- Location & working pattern
Gurugram, Haryana, India
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