Senior Platform Engineer

We are looking for a Senior Platform Engineer (AI/ML) to architect, build, and maintain our next-generation cloud infrastructure and MLOps platforms. In this role, you will bridge the gap between core infrastructure engineering and AI/ML data workflows, enabling data science teams to seamlessly train, serve, and monitor machine learning and LLM models in production at enterprise scale.

Responsibilities:

• Internal Developer Platform (IDP): Design, provision, and maintain secure, multi-tenant Kubernetes clusters (EKS/GKE) using Infrastructure as Code (Terraform).
• AI/ML Model Serving and Lifecycle: Build and operate production model serving pipelines using vLLM, KServe, or AWS Bedrock; manage model registries and experiment tracking using MLflow or Kubeflow.
• Vector and GenAI Infrastructure: Deploy and optimise Vector Databases (pgvector, Pinecone, OpenSearch) to support high-throughput RAG (Retrieval-Augmented Generation) workflows.
• Data Pipeline Orchestration: Orchestrate scalable ETL/ELT pipelines and continuous model retraining workflows using Apache Airflow, PySpark, and event streams (Kafka/PubSub).
• GitOps and Automation: Drive zero-downtime microservice deployments across environments using GitOps tools (ArgoCD, FluxCD) and automated CI/CD pipelines (GitHub Actions, Jenkins).
• Observability and Drift Monitoring: Implement unified monitoring and alerting across platform infrastructure and AI models using Prometheus, Grafana, Datadog, and ML drift tools (Evidently AI).

The core requirements for the job include the following:

Must-Have Technical Expertise (The 4 Pillars):

• Cloud and Kubernetes Foundation: Hands-on experience with AWS/GCP, Kubernetes (EKS or GKE), Docker, and modular Terraform infrastructure.
• AI / MLOps / LLM Platforms: Proven experience deploying ML models to production using KServe, vLLM, or AWS Bedrock; experience with Vector Databases (Pinecone/pgvector) and MLflow/Kubeflow.
• Data Pipelines: Hands-on expertise with Apache Airflow DAGs, data movement pipelines, and event streaming (Kafka or GCP Pub/Sub).
• CI/CD and Observability: Strong proficiency in ArgoCD (GitOps), GitHub Actions/Jenkins, and observability tools (Prometheus, Grafana, Datadog, or OpenTelemetry).

Preferred Background:

• Experience in regulated, high-security data environments (HealthTech, InsurTech, FinTech, or high-throughput B2B SaaS).
• Hands-on experience with secrets management (HashiCorp Vault, External Secrets Operator) and container security scanning tools (Trivy, Cosign).

Education and Certifications:

• B. Tech / M. Tech / BCA / MCA in Computer Science, Information Technology, or a related technical field.
• AWS Certified Solutions Architect or CKA (Certified Kubernetes Administrator) is a plus.