Time since we first saw it
Day 53
Similar jobs usually come down between day 6 and day 69.
From 4,284 similar postings that came down. Coming down is not always a hire.
Data Engineer
Reads like a ML Engineer role: putting models into production.
Bengaluru, Karnataka, India
Live, first seen by us 53 days ago- Pay
- Pay not listed
- Work setting
Hybrid — work setting source
Work setup in the employer’s posting: Hybrid
From the employer’s posting- Employment
Full-time — employment source
Employment type in the employer’s posting: Full time
From the employer’s posting
What you’ll work on
Full postingRebuild legacy Spark and Hadoop MapReduce workflows as production pipelines on AWS and Databricks
Design and deploy AI agents and workflow automation using LLMs such as Anthropic Claude
Build AI data pipelines for unstructured data ingestion and vector database metadata tagging
Maintain and troubleshoot existing Hadoop big data workloads, resolving bottlenecks and OOM errors
Fix BI dashboard latency and data extract issues for business stakeholders
From the employer’s posting
Deconstruct legacy Apache Spark and Hadoop MapReduce workflows to re-architect and rebuild them as optimized, production-ready pipelines within AWS and Databricks.
Design, build, and deploy intelligent AI agents and workflow automation tools leveraging leading Large Language Models (LLMs) such as Anthropic Claude.
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.
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.
performing minor functional enhancements, bug fixes, and data extract optimizations to resolve report dashboard latency.
Tools in this posting
- Python
- Scala
- SQL
- AWS
- Databricks
- Delta Lake
- Hadoop
- Looker
- Power BI
- S3
- Spark
- Tableau
- PySpark
Source — Tool mentions in context
ROLES AND 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 productio 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.
We are seeking an ambitious Data and AI Engineer with 5-8 years of experience to play a pivotal role in modernizing client’s 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. ROLES AND 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 productio 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.
We are seeking an ambitious Data and AI Engineer with 5-8 years of experience to play a pivotal role in modernizing client’s 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. ROLES AND 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 productio
Posting record
Report an error- Status in our records
- Live
- First seen by us
- Sep 12, 202653 days ago
- Recorded sightings
- 207
- Last seen by us
- Nov 4, 2026
- Employer says posted
- Sep 10, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
How this job compares
EXL Service is hiring
200 open jobs
Open jobs by the week we first saw them, last 12 weeks.
- Takes jobs down at a usual pace
- 5% of open jobs were reposted
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Has EXL Service posted this job before?
Yes. We’ve followed 16 other Data Engineer jobs there since August 2026. 6 came down, usually after about 7 days. 10 are still up.
Large employers often post one title for several teams, so these can be separate openings.
About the role
We file this job under ML Engineer, by the work the posting describes.
ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.
Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.
A data scientist often works out which model to build; an ML engineer makes it run reliably in production.
US government figures for computer and information research scientists
- Median yearly pay, May 2025
- $140,300
- Expected change, 2025 to 2035
- 21.8% more jobsagainst 3.5% for all jobs
The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.
Job description
We are seeking an ambitious Data and AI Engineer with 5-8 years of experience to play a pivotal role in modernizing client’s 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.
ROLES AND 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 productio
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.
Source details
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- Pay
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- Location & working pattern
Bengaluru, Karnataka, India
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