Hiring AI Engineer – GenAI Platform Automation

Job Description

AI Engineer – GenAI Platform Automation

Experience

10+ Years

Location

Remote – Pan India

Employment Type

Haparz Payroll

Work Mode

Remote

Notice Period

Immediate / Short Notice Preferred

About the Role

We are looking for a senior AI Engineer – GenAI Platform Automation to lead automation initiatives across enterprise Generative AI, Data Science, Data Engineering, and Analytics platforms.

The role focuses on building scalable, secure, and self-service automation capabilities across infrastructure provisioning, CI/CD, cloud environments, AI workload deployment, governance, observability, and operational excellence. The ideal candidate will have strong hands-on experience in platform engineering, cloud automation, DevOps, Infrastructure-as-Code, Python, and enterprise GenAI ecosystems.

Key Responsibilities

Lead end-to-end automation initiatives for enterprise GenAI, Data Science, Data Engineering, Metadata, Data Quality, Event Streaming, and Analytics platforms.

Design self-service automation for infrastructure provisioning, environment onboarding, deployment, governance, monitoring, and operational workflows.

Build automation capabilities supporting the AI lifecycle, including experimentation, model training, deployment, inference, observability, and lifecycle management.

Develop scalable Infrastructure-as-Code solutions using Terraform and cloud-native automation frameworks.

Design and maintain enterprise CI/CD pipelines, automated testing, deployment, and release processes using modern DevOps toolchains.

Automate Kubernetes, containers, serverless, and distributed computing environments in collaboration with cloud and platform engineering teams.

Develop automation solutions for GenAI and Agentic AI applications, including MCP-enabled services, API integrations, workflow automation, and event-driven architectures.

Implement observability, monitoring, logging, tracing, alerting, automated remediation, and reliability engineering practices.

Work with architecture, security, governance, engineering, and business teams to ensure enterprise standards and compliance requirements are met.

Conduct technical design reviews, automation assessments, code reviews, and establish engineering best practices.

Provide technical leadership and mentorship to engineering teams adopting automation-first and platform engineering practices.

What We’re Looking For

10+ years of hands-on experience in platform engineering, automation engineering, cloud engineering, DevOps, or distributed systems.

Strong experience building enterprise self-service platforms supporting AI/ML, Data Science, Data Engineering, or Advanced Analytics workloads.

Strong expertise in automation frameworks, CI/CD, DevOps, Infrastructure-as-Code, and software delivery lifecycle automation.

Hands-on experience with Terraform and cloud-native infrastructure automation.

Strong experience with Python for automation, orchestration, scripting, tooling, and platform engineering.

Experience with Bitbucket, Bamboo, Jira, Confluence, or similar enterprise DevOps toolchains.

Experience working with Kubernetes, containers, serverless platforms, YARN, and distributed processing environments.

Knowledge of Generative AI and Agentic AI architectures, MCP frameworks, APIs, workflow automation, and enterprise AI platforms.

Experience with event-driven architectures and technologies such as Kafka and streaming platforms.

Strong understanding of cloud engineering, networking, security, scalability, resilience, and cost optimization.

Experience implementing observability solutions covering monitoring, logging, tracing, alerting, and operational dashboards.

Understanding of metadata management, data lineage, data governance, and semantic-layer concepts is highly valuable.

Good to Have

Experience supporting enterprise GenAI platforms, AI governance, model management, and AI operationalization.

Experience with GitOps, DevSecOps, Platform Engineering, and Reliability Engineering practices.

Exposure to data governance, data quality, metadata management, and model lifecycle automation.

Experience creating reusable internal developer platforms and self-service engineering tools at enterprise scale.

Banking, AML, fraud detection, financial crime, or risk analytics domain experience is an advantage.