Lead Data & AI Ops Engineer, Data & AI Engineering

Pretium Enterprise Services, India; Bengaluru; On-site; INR 1,800,000 - 3,200,000; Full time

Benefits offered by this job

3 Days work from office

Job summary

Pretium Enterprise Services, India, is seeking a Lead Data & AI Operations Engineer to own operational health and governance of the Data & AI ecosystem. You will design, build, and deliver technical platform capabilities for reliability and cost efficiency.

The role combines data engineering with an operations mindset, emphasizing automation, observability, and AI/GenAI tooling to achieve enterprise-scale performance and resilience.

Qualifications

• Minimum 8+ years in Data engineering, DataOps, or production Data/AI operations.
• Hands-on Snowflake architecture, administration, and performance tuning.
• Experience building automation, governance, and platform controls.

Responsibilities

• Own end-to-end Data & AI Operations across data platforms and AI workloads.
• Provide Snowflake production support: monitoring, troubleshooting, RBAC, and health checks.
• Develop data pipelines, orchestration, and engineered solutions for reliability.
• Monitor AI/ML workloads in production and manage dependencies and API integrations.
• Develop automated remediation, self-healing workflows, and AI-assisted runbooks.
• Implement intelligent monitoring, anomaly detection, and DevOps practices.
• Lead incident, change, and release management with RCA documentation.

Skills

Snowflake

Data Engineering

FinOps

Python

APIs

RPA

CI/CD

DataOps

MLOps

AIOps

DevOps

Power BI

Looker

Tools

Fivetran

Informatica

Azure Data Factory

Job description

Role Overview

We are seeking a high-potential, hands‑on Lead Data & AI Operations Engineer to own and continuously improve the operational health, governance, controls, reliability, and efficiency of our enterprise Data & AI ecosystem.

This is a high‑impact technical leadership role with end‑to‑end accountability for Data & AI Operations across the company. The successful candidate will establish the operating model, engineering controls, automation, observability, and governance required to run Data & AI platforms as reliable, secure, and cost‑efficient enterprise services.

The ideal candidate combines deep Snowflake and Data Engineering expertise with a strong operations and controls mindset. This engineer will also design, build, and deliver technical solutions and platform capabilities required to achieve operational excellence and efficiency goals.

Key Responsibilities

• Supported end‑to‑end Data & AI Operations and Production Support across enterprise data platforms, data pipelines, analytics, BI, and AI/ML workloads, ensuring availability, reliability, performance, and SLA adherence.
• Provided day‑to‑day Snowflake production support and administration , including workload monitoring, query performance analysis, troubleshooting, access/RBAC management, capacity monitoring, and platform health checks.
• Supported and enhanced Data Engineering pipelines and workflows , troubleshooting data ingestion, transformation, orchestration, processing, and downstream data delivery issues across production environments.
• Supported AI/ML and GenAI workloads in production , including monitoring application and model‑related jobs, data dependencies, API integrations, scheduled processes, failures, and overall operational health.
• Contributed to AI Operations (AIOps) capabilities by using AI/GenAI tools for incident analysis, log summarization, anomaly identification, troubleshooting assistance, knowledge retrieval, and faster root‐cause analysis.
• Developed Python scripts, APIs, workflow automation, RPA, and AI‑assisted automation to reduce repetitive operational activities, automate health checks and validations, accelerate issue resolution, and improve support productivity.
• Supported the implementation of intelligent monitoring and anomaly detection across data pipelines, Snowflake workloads, and AI services to proactively identify failures, performance degradation, unusual patterns, and operational risks.
• Assisted in developing automated remediation and self‑healing operational workflows for common production issues, reducing manual intervention and improving the Resolution SLA.
• Used GenAI‑based operational assistants to support troubleshooting, incident summarization, RCA preparation, log analysis, runbook recommendations, and knowledge management activities.
• Monitored production data pipelines, ETL/ELT jobs, orchestration workflows, AI workloads, APIs, and platform services , investigated failures, performed impact analysis, and coordinated timely service restoration.
• Performed data quality checks, reconciliation, validation, and root‑cause analysis to identify data discrepancies and ensure accurate, complete, and reliable data delivery to downstream applications and AI/analytics workloads.
• Supported enterprise data platform controls covering data quality, access, security, privacy, metadata, lineage, change management, and production readiness.
• Monitored Snowflake and cloud consumption, performance, and utilization , identified inefficient queries and workloads, and supported optimization initiatives to improve performance and control platform costs.
• Built and maintained observability, monitoring, alerting, operational dashboards, automated health checks, and proactive notifications across Data and AI platforms.
• Managed Incident, Problem, Change, and Release Management activities, including production troubleshooting, service restoration, RCA documentation, change validation, deployment support, and permanent remediation of recurring issues.
• Supported DataOps, MLOps, AIOps, and DevOps practices , including CI/CD pipelines, testing, deployment, release validation, version control, monitoring, documentation, and production support.
• Worked closely with Data Engineering, Analytics, BI, AI/ML, Architecture, Security, Infrastructure, and business teams to troubleshoot production issues, manage dependencies, and implement platform improvements.
• Participated in on‑call and production support activities , ensuring critical Data and AI incidents were addressed within agreed SLAs and appropriately communicated to stakeholders.
• Identified recurring operational issues and implemented automation, AI‑assisted solutions, process improvements, and permanent fixes to reduce manual effort, prevent repeat incidents, and improve production stability.
• Contributed to continuous improvement by promoting operational discipline, automation‑first practices, documentation, reusable runbooks, knowledge sharing, and Data/AI production support best practices.

What We Are Looking For

• 8-12 years of experience across Data Engineering, Data Platforms, Data Ops, Cloud Engineering, or Production Operations, with demonstrated technical leadership.
• Deep hands‑on Snowflake expertise , including architecture, administration, SQL, performance tuning, workload management, security/RBAC, monitoring, troubleshooting, and optimization.
• Strong experience designing and building engineering solutions , not just administering or supporting Data Platforms.
• Demonstrated FinOps and cost optimization experience , with measurable outcomes in Snowflake/cloud consumption reduction, workload optimization, cost attribution, and efficiency improvement.
• Strong experience building automation using RPA platforms, Python, APIs, workflow automation, and AI/GenAI tools.
• Strong expertise with dBT and enterprise ETL/ELT technologies such as Fivetran, Informatica, and Azure Data Factory.
• Experience implementing DataOps, CI/CD, observability, data quality, governance, metadata, lineage, and automated platform controls.
• Strong understanding of production operations, incident/problem management, RCA, change management, and platform reliability engineering.
• Experience with enterprise BI platforms such as Power BI and Looker.
• Ability to operate as both a hands‑on engineer and technical leader/manager , taking problems from identification through solution architecture, engineering, implementation, and measurable business outcome.

NOTES

• Prefer candidates already residing in Bangalore.
• Standard Shift Timing is 12noon to 9pm, however this may vary depending on the business requirements.
• 3 Days work from office.
• Weekend on call support is required.