AI Engineer - Senior Associate

V2 Solutions; Hyderabad; On-site; INR 1,200,000 - 1,800,000; Full time

Job summary

V2 Solutions in Hyderabad is seeking a proficient Python data engineer with strong SQL skills and hands-on experience in data warehousing and automation.

You will build and maintain automated CI/CD pipelines, work with AWS/Azure/GCP for compute, storage, IAM, and automation, and optimize data workflows using Databricks assets while applying guardrails for LLMs and observability.

Qualifications

• Proficient in Python for data manipulation and automation.
• Experience with Git and CI/CD pipelines.
• Hands-on expertise with cloud platforms (AWS/Azure/GCP) and infrastructure automation.
• Familiar with Databricks Asset Bundles and data workflow optimization.
• Knowledge of LLM frameworks and guardrails for data privacy and validation.

Responsibilities

• Build and maintain automated pipelines for build, test, deployment, and rollback.
• Work with AWS/Azure/GCP for compute, storage, IAM/RBAC and automation.
• Leverage Databricks Asset Bundles to optimize data workflows.
• Develop evaluation pipelines and guardrails for LLMs including PII redaction and output validation.
• Implement LLM observability with tracing, metrics, and cost management.

Skills

Python

Tools

SQL

Git

CI/CD

AWS

Azure

GCP

Databricks

LLM frameworks

Job description

Role & responsibilities

Proficient in Python (typing, pytest, logging, async) with hands‑on experience in Mest, and skilled in SQL for data manipulation.

• Experienced with Git and CI/CD platforms (GitHub Actions, Jenkins, Azure DevOps, or GitLab CI), building automated pipelines for build, test, deployment, and rollback.
• Hands‑on expertise with AWS, Azure, or GCP for compute, storage, IAM/RBAC, secrets management, and infrastructure automation using AWS CDK and Terraform.
• Utilized Databricks Asset Bundles to optimize data workflows and accelerate data processing.
• Knowledgeable in LLM frameworks such as LangGraph, CrewAI, or Strands, including building evaluation pipelines and applying guardrails for PII redaction, injection defense, and output validation.
• Implemented LLM observability with tracing, metrics (token, latency, cost), failure analysis, and managed cost/quota strategies including TPM/RPM limits and caching.