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GenAI Engineer

Vrinda International

Job Location:

Gurgaon - India

Monthly Salary: ₹ 18 - 22

Experience Required: 5-7years

Posted: 25 June 2026 (30+ days ago)

Application Deadline: 22 September 2026

Vacancies: 1 Vacancy

Job Summary

Hiring Alert GenAI Engineer (Immediate Joiners Only)

location: Gurugram

Experience: 47 Years

Budget: Up to 22 LPA (Flexible)

Notice Period: Immediate to 710 Days (Serving only)

Job Overview

We are looking for a skilled GenAI Engineer with strong backend engineering expertise and hands-on experience in building production-grade AI/ML systems using modern GenAI stacks AWS services and RAG-based architectures.

Requirements

Key Skills Required

Core Engineering

Strong proficiency in Python (async typing packaging unit testing)
Strong understanding of Data Structures & Algorithms
Experience with Microservices & Event-driven architecture

Cloud & AWS

Hands-on experience with:
- Amazon Bedrock
- SageMaker
- S3 Lambda Step Functions
- CloudWatch IAM

GenAI / LLM Stack

RAG architectures (design indexing retrieval evaluation)
LangChain / LangGraph
Prompt engineering & evaluation techniques
Guardrails (PII/Secrets redaction policy enforcement)
HITL (Human-in-the-loop) workflows
Vector DBs: pgvector / OpenSearch Vector

Data & Storage

ETL pipelines using Pandas / Spark
Databases: PostgreSQL DynamoDB NoSQL systems

DevOps & Engineering Practices

Git CI/CD (GitHub Actions / Jenkins)
Docker basic Kubernetes
Logging monitoring tracing & observability

Bonus Skills

ReactJS for full-stack prototypes
Agentic workflows (tool calling task decomposition)
SageMaker tuning / feature stores
Prompt evaluation frameworks & cost monitoring dashboards

Required Skills:

Key Skills Required Core Engineering Strong proficiency in Python (async typing packaging unit testing) Strong understanding of Data Structures & Algorithms Experience with Microservices & Event-driven architecture Cloud & AWS Hands-on experience with: Amazon Bedrock SageMaker S3 Lambda Step Functions CloudWatch IAM GenAI / LLM Stack RAG architectures (design indexing retrieval evaluation) LangChain / LangGraph Prompt engineering & evaluation techniques Guardrails (PII/Secrets redaction policy enforcement) HITL (Human-in-the-loop) workflows Vector DBs: pgvector / OpenSearch Vector Data & Storage ETL pipelines using Pandas / Spark Databases: PostgreSQL DynamoDB NoSQL systems DevOps & Engineering Practices Git CI/CD (GitHub Actions / Jenkins) Docker basic Kubernetes Logging monitoring tracing & observability Bonus Skills ReactJS for full-stack prototypes Agentic workflows (tool calling task decomposition) SageMaker tuning / feature stores Prompt evaluation frameworks & cost monitoring dashboards

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