EXL

Agentic AI Data Engineer

EXL · Pune, Maharashtra, India

Business Consulting and Services · 10,001+ employees

6 h ago
Senior (5-10 yrs) Full-time India
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About the role

Design and implement autonomous agentic AI systems to orchestrate complex data workflows and decision-making pipelines. Collaborate with cross-functional teams to deploy scalable, LLM-powered applications while ensuring data quality and security.

What they look for

Python SQL AWS LLMs Generative AI Agentic AI Data Engineering Spark Kubernetes Docker RAG Vector Databases ETL/ELT Machine Learning Cloud Architecture Data Lakes

Requirements

Requires 5-10 years of experience in data or ML engineering with strong proficiency in Python, SQL, and AWS cloud services. Candidates must have hands-on experience with LLMs, agent frameworks, and distributed data processing technologies.

Full 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