Agentic AI Data Engineer
Hybrid Gurugram Full-time Mid-level Data Engineering
At a glance
Mid-level Data Engineering role at Fa Ewjt Saasfaprod1. Gurugram · full-time.
Pay not stated Data Engineering salary
RoleData Engineering
SeniorityMid-level
LocationGurugram
WorkplaceHybrid
EmploymentFull-time
PostedJul 30, 2026
Role brief
dataroles summary, based on the employer's posting.
What you'll do
- Build agents that coordinate data workflows and decision processes, then create pipelines for batch and live data
- Develop LLM applications with retrieval, tool use and multi-agent approaches, connecting AWS AI services for production use
- Design data lakes, warehouses and lakehouses, plus APIs and microservices that make AI capabilities accessible
- Protect data quality and security, and add monitoring and logging for agents and pipelines
- Work with data scientists, ML engineers and product teams to deploy solutions and improve cloud workload cost and speed
What you bring
- You have five to ten years of overall experience, including at least four years in data or ML engineering
- You bring strong Python and SQL skills, with experience in ETL or ELT and distributed data processing
- You have worked with LLMs and generative AI, including agent frameworks, RAG, embeddings and vector databases
- You have hands-on experience delivering production AI/ML systems and AWS services such as S3, Glue or Lambda
Who this fits
This role suits a mid-level engineer who has built production AI/ML systems and can work across data engineering and agent-based applications. It is a full-time hybrid role in Gurugram, India.
From the employer
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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