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GEN AI Engineer

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Onsite

GEN AI Engineer

Quarks Technosoft

GEN AI Engineer

Skills

Google Cloud PlatformSQL QueriesData EngineeringPythonMachine LearningAIdbt

1 month ago

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Important information


Contract type:

Permanent

Location:

Bengaluru, Karnataka, India

Work mode:

Onsite

Published on:

23 September 2026

What they need


">GEN AI Engineer

5-7 Years Bengaluru

  • GEN AI
  • LLM
  • Python
  • SQL
  • GCP
  • Vertex AI
  • DBT

Exp Required- 5+ Years

Role Objective

You will build a suite of AI-driven Accelerators and Autonomous Agents designed to automate the entire lifecycle of our Data Platform-from legacy migration and pipeline construction to incident response and ML lifecycle management. Your goal is to turn the Data Platform into a self-service, self-healing ecosystem.

Key Focus Areas Deliverables

  1. Migration Construction Accelerators
  • Legacy to dbt Engine: Build agents to parse legacy SQL, Stored Procedures, and ETL tool exports into modular, optimized dbt models.
  • Copilot for Pipelines: Develop an internal "Pipeline Assistant" that generates boilerplate Spark/Python code and dbt transformations based on metadata and business requirements.
  1. Autonomous Incident Problem Solver
  • Self-Healing Ops: Build agents that monitor platform logs (Spark, dbt, Cloud-native) to identify failures, explain the root cause in plain language, and automatically propose (or commit) a fix.
  • On-call Assistant: An AI agent that triages data quality alerts, identifies upstream dependencies, and notifies affected downstream stakeholders.
  1. Machine Learning (MML) Construction
  • Auto-MML: Develop agents to accelerate the "MML" (Machine Learning Modeling) lifecycle-from automated feature engineering suggestions to skeleton code for training and deployment.

Technical Stack Requirements

AI Agentic Frameworks (Must Have)

  • Agent Orchestration: Expertise in LangChain, CrewAI, or AutoGPT to build multi-agent workflows.
  • Code Generation: Proven experience using LLMs for Code-to-Code translation (specifically SQL to dbt/Python).
  • RAG Mastery: Building advanced Retrieval-Augmented Generation systems over technical documentation, data catalogs, and codebases.

Data Engineering Platform

  • Expert dbt: Deep knowledge of dbt Jinja, Macros, and Mesh-essential for teaching an AI how to write "good" code.
  • Spark Lakehouse: Understanding of Spark internals to build agents that can optimize job performance and resolve OOM (Out of Memory) errors.
  • Cloud Infrastructure: Experience with GCP (Vertex AI) or equivalent cloud AI suites

Disclaimer: This job posting and location has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

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