AI Engineer – GenAI Platform Automation
Experience: 10+ Years
Location: Chennai / Pune – Hybrid, 3 Days WFO
Payroll: Haparz
Budget: Up to 30 LPA
Role Overview
We are looking for a senior AI Engineer – GenAI Platform Automation to lead automation and platform engineering initiatives across enterprise Generative AI, Data Science, Data Engineering, and Advanced Analytics environments.
The role focuses on building scalable, secure, and reusable automation capabilities across infrastructure provisioning, CI/CD, environment management, governance, observability, testing, deployment, and AI workload enablement.
The ideal candidate will combine strong platform automation, cloud engineering, DevOps, Infrastructure-as-Code, Python, and GenAI ecosystem experience with the ability to drive automation-first engineering practices across enterprise platforms.
Key Responsibilities
- Lead end-to-end automation initiatives for GenAI, Data Science, Metadata, Data Quality, Event Streaming, and Analytics platforms.
- Design and build self-service capabilities for environment provisioning, application onboarding, deployment, governance, monitoring, and operational workflows.
- Automate the complete AI/analytics lifecycle covering data preparation, experimentation, model training, deployment, inference, monitoring, and lifecycle management.
- Develop reusable Infrastructure-as-Code solutions using Terraform and related automation frameworks.
- Design and maintain enterprise CI/CD pipelines, automated testing, release automation, and deployment workflows.
- Work with cloud and platform engineering teams to automate Kubernetes, containers, serverless platforms, YARN, and distributed computing environments.
- Build automation capabilities for GenAI and Agentic AI applications, including MCP-enabled services, APIs, event-driven workflows, and AI platform integrations.
- Implement automation for platform monitoring, observability, alerting, automated remediation, performance optimization, and reliability engineering.
- Develop Python-based automation, orchestration, scripting, tooling, and operational solutions.
- Implement and maintain DevSecOps controls throughout infrastructure and application delivery pipelines.
- Collaborate with architecture, security, governance, engineering, data science, and business teams to ensure enterprise compliance and engineering standards.
- Conduct technical design reviews, automation assessments, code reviews, and establish reusable engineering standards.
- Provide technical leadership and mentorship to engineering teams adopting platform engineering and automation-first practices.
- Support strategic enterprise AI initiatives, including AI/analytics platforms supporting AML and financial-risk use cases.
Required Experience
- 10+ years of hands-on experience in Platform Engineering, Automation Engineering, Cloud Engineering, DevOps, or Distributed Systems.
- Strong experience designing and implementing enterprise automation frameworks and self-service platforms.
- Hands-on experience with CI/CD, DevOps, Infrastructure-as-Code, and software delivery automation.
- Strong experience with Terraform and cloud infrastructure automation.
- Hands-on experience with Atlassian DevOps ecosystem, particularly Bitbucket, Bamboo, Jira, and Confluence.
- Strong Python development experience for automation, orchestration, scripting, and engineering tools.
- Experience with cloud-native architectures, containers, Kubernetes, serverless, and distributed computing.
- Experience automating deployments and operations across Kubernetes/containerized and distributed environments.
- Experience with Kafka or other event-streaming platforms and event-driven architectures.
- Strong understanding of enterprise AI/Data Science platform architecture, including compute and storage separation, Jupyter, VS Code, virtual environments, containers, and developer productivity tooling.
- Working knowledge of Generative AI, Agentic AI, MCP frameworks, API integrations, and AI workflow automation.
- Experience with observability covering logging, monitoring, tracing, alerting, dashboards, and operational automation.
- Understanding of metadata management, data lineage, governance, data quality, and semantic-layer concepts.
- Strong understanding of cloud networking, security, scalability, resilience, infrastructure management, and cost optimization.
- Excellent communication skills with the ability to work with engineers, architects, product owners, and business stakeholders.
Preferred Experience
- Enterprise Generative AI platform engineering or AI operationalization experience.
- Experience with AI governance, model management, or ML/AI lifecycle platforms.
- Experience with GitOps, DevSecOps, Reliability Engineering, and Platform Engineering practices.
- Experience with data governance, data quality, metadata management, or model lifecycle automation.
- Exposure to large-scale enterprise cloud and data platforms.
- Experience building Internal Developer Platforms, reusable engineering tools, or self-service developer portals.
- Banking, AML, fraud detection, financial crime, risk analytics, or related domain experience is advantageous.
Education
Bachelor’s or Master’s degree in Computer Science, Engineering, Information Technology, or a related technical discipline.
Work Location: Hybrid remote in Pune, Maharashtra