Senior AI Platform Engineer

Overview

Design, build and maintain scalable, secure and highly available AWS infrastructure for AI/ML workloads using Terraform, automate release pipelines, and build observability and alerting solutions. Operates with a high degree of autonomy and mentors engineers across the organization.

What you'll do

• Design, build, and maintain scalable, secure, and highly available AWS infrastructure (EKS, Lambda, ECS, VPC, IAM) for AI/ML workloads using Terraform best practices.
• Own and continuously evolve GitLab CI/CD pipelines for AI platform services to enable fast, reliable, and repeatable automated releases.
• Architect a centralized observability stack using Prometheus and Grafana with golden-signal dashboards and design intelligent alerting strategies to lead efficient incident response.
• Define and track DORA metrics to drive delivery and reliability improvements while building self-healing, auto-scaling, and cost-optimized infrastructure for AI/ML services on Kubernetes.
• Implement infrastructure security best practices, establish platform governance standards, partner on production-grade AI/RAG deployments, and mentor junior/mid-level engineers.

What you'll need

• Foundational understanding of AI/ML technologies and experience leveraging, securing, or positioning AI-driven solutions to optimize outcomes within your functional domain.
• 4+ years of experience as a Platform, SRE, or DevOps Engineer.
• 1+ years supporting AI/ML or data platform infrastructure.
• Deep hands-on AWS skills (EKS, Lambda, ECS, VPC, IAM, S3).
• Strong hands-on experience using Terraform (modules, workspaces, remote state) for Infrastructure as Code.
• Experience designing and maintaining GitLab CI/CD pipelines with runners and automated deployments.
• Proven expertise in centralized monitoring using Prometheus and Grafana (custom exporters, dashboards, alerting).
• Experience tuning alerting systems (Alertmanager, PagerDuty, OpsGenie).
• Solid, hands-on understanding of DevOps/DORA metrics (deployment frequency, lead time for changes, change failure rate, MTTR).
• Experience operating containerized apps on Kubernetes (EKS, Helm, Karpenter/HPA).
• Strong scripting skills in Python (and/or Go/Bash).
• Excellent communication skills.

Nice to have

• Demonstrated ability to leverage AI technologies and workflows to drive measurable operational efficiency.
• Experience with LiteLLM (multi-region deployments), Ray.io/Anyscale, and Temporal.
• Experience with agentic frameworks (LangGraph, LangChain, or LlamaIndex).
• Working knowledge of vector databases (Qdrant, Pinecone, Weaviate) and memory/context layers like ZEP.
• MLOps practices (model registry, MLflow/Kubeflow) and LLM observability tools (Arize Phoenix).
• Exposure to GitOps workflows (ArgoCD/Flux), log aggregation and distributed tracing (Loki, ELK, Jaeger/Tempo), cloud cost optimization (FinOps), and security compliance frameworks (SOC 2, ISO 27001).

Details

• Location: Pune, India.
• Work mode: Hybrid.