Job Description

Agentic AI Data Engineer

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

About EXL

Choosing a digital partner is about more than capabilities — it’s about collaboration and character.

Unrealistic overhauls and off-the-shelf products ignore what matters most — your unique needs, culture, goals, and your legacy data and technology environments.

At EXL, our collaboration is built on ongoing listening and learning to adapt our methodologies. We’re your business evolution partner—tailoring solutions that make the most of data to make better business decisions and drive more intelligence into your increasingly digital operations.

Whether your goals are scaling the use of AI and digital, redesign operating models, or driving better and faster decisions, we’re here to partner with you to help you gain—and maintain—competitive advantage with efficient, sustainable models at scale.

Our expertise in transformation, data science, and change management helps make your business more efficient and effective, improve customer relationships and enhance revenue growth. Instead of focusing on multi-year, resource- and time-intensive platform designs or migrations, we look deeper at your entire value chain to integrate strategies with impact.

We use our specialization in analytics, digital interventions, and operations management—alongside deep industry expertise — to deliver solutions that help you outperform the competition.

At EXL, it’s all about outcomes—your outcomes—and delivering success on your terms. Share your goals with us and together, we’ll optimize how you leverage data to drive your business forward.

For more information, visit www.exlservice.com.

Industry

Consulting & Advisory

Company Size

10,000+ employees

Headquarters

New York, NY

Year Founded

Unknown

Website

exlservice.com

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