Data Analytics-5-7Yrs-PAN INDIA

Crescendo Global Leadership Hiring India; Pune District, Bengaluru, Delhi; Hybrid; INR 3,000,000 - 4,200,000; Full time

Job summary

Crescendo Global Leadership Hiring India seeks a Data and AI Engineer with 5-8 years of experience to modernize client data infra and scale AI capabilities. You will bridge legacy Hadoop with a cloud-first Databricks/AWS ecosystem, architecting pipelines and deploying AI agents using Anthropic Claude.

You will also optimize BI reporting and contribute to the AI stack across data ingestion, feature tokenization, and vector databases.

Qualifications

• 5-8 years of professional software/data engineering experience.
• Experience building AI agents and integrating LLM APIs (Anthropic Claude).
• Strong Python (PySpark) or Scala and advanced SQL skills.
• Hands-on with AWS and Databricks environments.

Responsibilities

• Modernize legacy Hadoop/Spark workflows into production-ready AWS/Databricks pipelines.
• Design, build, and deploy AI agents and automation using LLMs like Anthropic Claude.
• Engineer AI data pipelines with unstructured data ingestion and vector databases.
• Maintain Hadoop workloads during migration and resolve bottlenecks/OOM errors.
• Collaborate with BI teams to optimize data extracts and reporting performance.
• Leverage Databricks features (ACID, Z-Ordering, caching) for performance/cost.
• Implement Databricks AI Suite tools (Vector Search, Mosaic AI, Lakeflow) for GenAI/RAG apps.

Skills

AI Agents & LLMs

Distributed Computing

Python (PySpark)

SQL (advanced)

Cloud & Databricks

BI Tools (Tableau/Looker/Power BI)

Education

Bachelor’s degree in CS/Data Engineering/IS

Tools

AWS (S3, Glue, EMR)

Databricks (Delta Lake, Unity Catalog)

Hadoop/Spark/Hive

Job description

Role Overview

We are seeking an ambitious Data and AI Engineer with 5-8 years of experience to play a pivotal role in modernizing clients core data infrastructure and scaling advanced AI capabilities. This role bridges legacy big data environment and future cloud platform. You will actively maintain and optimize our existing data pipelines while architecting, developing, and transitioning workflows to an AI-ready cloud ecosystem. Beyond traditional data pipelines, you will play an active role in building and deploying intelligent AI agents and leveraging advanced Large Language Models (LLMs) like Anthropic Claude, utilizing the cutting-edge Databricks AI suite to deliver immediate business value.

• Experience Level: 5-8 Years
• Current Architecture: Hadoop (HDFS), Apache Spark, MapReduce, Hive
• Target Architecture: AWS (S3, Glue, EMR), Hive, HDFS, Airflow, ControlM, Databricks (Delta Lake, Unity Catalog, Vector Search, Mosaic AI)
• Core AI Stack: Anthropic Claude, Copilot, LangChain GenAI Agents
• BI & Analytics Stack: Tableau Desktop & Tableau Server

Core Responsibilities

• Cloud Modernization & Migration: Deconstruct legacy Apache Spark and Hadoop MapReduce workflows to re-architect and rebuild them as optimized, production-ready pipelines within AWS and Databricks.
• AI Agent & LLM Development: Design, build, and deploy intelligent AI agents and workflow automation tools leveraging leading Large Language Models (LLMs) such as Anthropic Claude.
• AI Data Pipeline Engineering: Build and optimize pipeline architectures explicitly tailored for AI use cases, including unstructured data ingestion, real-time feature tokenization, and metadata tagging for vector databases.
• Legacy Infrastructure Maintenance: Monitor, maintain, and troubleshoot existing big data workloads running on our Hadoop cluster to guarantee data availability for business operations during the multi-phase migration, resolving bottlenecks and Out-Of-Memory (OOM) errors.
• BI Engineering & Support: Act as the primary engineering liaison for downstream business stakeholders utilizing BI tools (e.g. Tableau, Looker, etc.) by performing minor functional enhancements, bug fixes, and data extract optimizations to resolve report dashboard latency.
• Cloud Optimization: Utilize Databricks and Delta Lake features (e.g., ACID transactions, Z-Ordering, caching) to significantly improve pipeline performance, reliability, and cost efficiency.
• Databricks AI Suite Implementation: Leverage Databricks tools (such as Databricks Vector Search, Mosaic AI, and Lakeflow) to orchestrate, track, and serve production-grade Generative AI and Retrieval-Augmented Generation (RAG) applications.

Required Qualifications

• Experience: 5 to 8 years of professional software engineering or data engineering experience in a production environment.
• AI & Agentic Frameworks: Hands-on experience or deep technical familiarity building functional AI agents, integrating LLM APIs (specifically Anthropic Claude), and utilizing orchestration frameworks (e.g., LangChain, Databricks Mosaic AI Agent Framework or any other tool ).
• Distributed Computing: Foundational understanding of distributed storage and computing concepts—specifically partitioning, shuffling, caching, and broadcast joins. Solid hands-on experience with Apache Spark is required.
• Programming & SQL: Strong proficiency in Python (PySpark) or Scala, alongside intermediate-to-advanced SQL querying capabilities (window functions, query tuning, and complex joins).
• Cloud & Databricks Exposure: Direct experience or deep theoretical knowledge of the AWS ecosystem (S3, IAM) and Databricks environments.
• Visualization Layer: Practical experience working with any BI tool (e.g. Tableau, Looker, Power BI, etc.) with the capability to debug calculated fields, modify parameters, and troubleshoot slow-loading reports.
• Education: Bachelor’s degree in Computer Science, Data Engineering, Information Systems, or a related quantitative field, or equivalent practical experience.