EXL

Analyzes business data and runs outsourced operations

Agentic AI Data Engineer

Data EngineeringConfirmed live in the last 24 hours

Full-timeGurugram, Haryana, IndiaNo salary listedIn-person

About the Job

Requirements

Requires 5–10 years of total experience.
Requires a bachelor's or master's degree in Computer Science, Engineering, or a related field.
Requires 4+ years of experience in data engineering or machine learning engineering.
Requires hands-on experience with production-grade AI/ML systems.
Requires strong experience with AWS services, including Amazon S3, AWS Glue, AWS Lambda, AWS Step Functions, Amazon Redshift or Athena, Amazon SageMaker, and Amazon Bedrock.
Requires experience with large language models and generative AI systems.
Requires hands-on experience with agent frameworks, including multi-agent orchestration, tool calling, or planning systems.
Requires familiarity with AgentCore or agent orchestration platforms.
Requires understanding of retrieval-augmented generation architectures, embeddings, and vector databases.
Requires experience with model deployment, inference optimization, and prompt engineering.
Requires strong proficiency in Python and SQL.
Requires experience with ETL/ELT tools and frameworks.
Requires distributed data processing experience with Spark, PySpark, or similar technologies.
Requires experience with streaming technologies such as Kafka or Kinesis.
Requires experience in data modeling and schema design.
Requires experience with vector databases such as Pinecone, FAISS, or OpenSearch.
Requires knowledge of data lakehouse architectures such as Delta Lake, Iceberg, or Hudi.
Requires experience with Docker and Kubernetes.
Requires experience with CI/CD for machine learning and data pipelines.
Requires strong problem-solving and system design skills.
Requires effective communication and cross-functional collaboration.

Responsibilities

Design and implement agentic AI systems that autonomously orchestrate data workflows and decision pipelines.
Build scalable data pipelines for structured and unstructured data in batch and real time.
Develop and manage LLM-powered applications using retrieval-augmented generation, 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 the cost and performance of cloud-based AI workloads.
Build autonomous data pipelines that self-heal and optimize.
Build AI agents capable of reasoning over enterprise data.
Build scalable LLM-powered applications integrated with business workflows.
Build intelligent systems that move beyond automation into decision-making.

Desired 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.
Experience with reinforcement learning or planning systems.
Background in distributed systems design.
Contributions to open-source AI/data projects.
AWS certifications such as Solutions Architect or Machine Learning Specialty.

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

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

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

About the Company

EXL

Analyzes business data and runs outsourced operations

EXL analyzes business data and runs outsourced operations for insurers, healthcare payers, banks, retailers, and other large organizations. Its teams combine industry expertise with data engineering and AI to handle tasks such as claims processing, payment reviews, customer service, and finance within existing technology environments. The company also builds software, including LifePRO, which manages insurance policies, and EXLdata.ai, which connects fragmented data for use in AI systems. Clients pay for consulting projects, ongoing managed services, and software delivered through the cloud. EXL was founded in 1999 and is headquartered in New York City.

Company size: 10,001+
Company stage: IPO
Headquarters: New York City, New York
Founded: 1999

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