GEN AI Engineer

Quarks Technosoft Pvt. Ltd.; Bengaluru; On-site; INR 4,500,000 - 6,000,000; Full time

Job summary

Quarks Technosoft Pvt. Ltd. is seeking a GEN AI Engineer in Bengaluru with 5+ years of experience to build AI-driven accelerators and autonomous agents for the data platform lifecycle.

Responsibilities include migration tooling, self-healing incident solvers, and ML lifecycle automation using GCP, Vertex AI, and dbt. The role emphasizes multi-agent frameworks and code generation capabilities.

Qualifications

• 5+ years of Gen AI experience and cloud AI tooling expertise.
• Proficient in building AI-driven accelerators and agents.
• Strong understanding of data platforms, dbt, and Spark.

Responsibilities

• Develop Migration Construction Accelerators for data pipelines.
• Create Autonomous Incident Solvers to diagnose and fix issues.
• Build Auto-MML workflows to accelerate ML lifecycle.

Skills

GEN AI

LLM

Python

SQL

GCP

Vertex AI

DBT

Tools

LangChain

CrewAI

AutoGPT

dbt

Spark

Vertex AI

Job description

>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.

2. 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.

3. 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.