ai - DevOps Engineer - CI/CD Pipeline

8 hours ago


Pune, Maharashtra, India intellimation Full time ₹ 12,00,000 - ₹ 36,00,000 per year

What are we looking for?
We're seeking an experienced DevOps Engineer to join our engineering team and help design, build, and scale secure, high-performance infrastructure for our Cognitive Automation product. This role is not just about managing pipelines and cloud platforms-it's about architecting resilient systems, driving automation, and enabling seamless deployments across global financial firms. We're looking for self-driven, energetic professionals eager to innovate in a fast-paced, domain-tech environment.

Responsibilities

  • Design, build, and maintain scalable CI/CD pipelines using tools like Jenkins, GitLab CI, or GitHub Actions.
  • Manage and optimize cloud infrastructure on AWS and Azure.
  • Automate infrastructure provisioning using Infrastructure as Code (IaC) tools such as Terraform or CloudFormation.
  • Implement and manage containerization with Docker and orchestration with Kubernetes.
  • Monitor system performance, reliability, and security using tools like Prometheus, Grafana, ELK, or similar.
  • Collaborate with development, QA, and product teams to streamline development-to-deployment workflows.
  • Perform root cause analysis for production errors and implement fixes and improvements.
  • Design and implement cost-effective infrastructure solutions that optimize performance, scalability, and resource utilization across cloud and hybrid environments.
  • Ensure High Availability (HA) and fault tolerance in all infrastructure components to meet business continuity and uptime requirements.
  • Build and manage secure infrastructure in line with financial industry regulations (e.g., PCI-DSS, SOC 2, ISO 27001), applying security-by-design principles across the DevOps lifecycle.
  • Proven experience in migrating infrastructure and workloads from On-Premise to Cloud and Cloud to On-Prem, with deep understanding of hybrid cloud architecture, network connectivity, and data transfer strategies.
  • Hands-on familiarity with most major cloud services and features in AWS and Azure, including but not limited to compute, storage, networking, IAM, monitoring, autoscaling, disaster recovery, and cost management tools.

Requisites

  • Bachelor's degree in computer science, Engineering, or related field (or equivalent experience).
  • 5+ years of experience in a DevOps or Site Reliability Engineering (SRE) role.
  • Hands-on experience with AWS and Azure cloud platforms (compute, storage, networking, IAM).
  • Strong knowledge of CI/CD tools (e.g., Jenkins, GitLab CI/CD, GitHub Actions, Azure DevOps).
  • Experience with Docker, Kubernetes, and container orchestration.
  • Proficiency in scripting languages such as Bash, Python, or PowerShell.
  • Experience with monitoring and logging tools (e.g., Prometheus, Grafana, ELK stack, CloudWatch).
  • Solid understanding of networking concepts, load balancing, and DNS.
  • Experience in managing hybrid or multi-cloud environments.
  • Familiarity with SIEM tools, vaulting solutions (e.g., HashiCorp Vault), and network security.
  • Knowledge of financial data security standards: PCI-DSS, GDPR, SOX, etc.
  • Hands on experience in writing deployment pipeline files/script.

Good To Have

  • Certifications like AWS Certified DevOps Engineer, Azure DevOps Engineer Expert, etc.
  • Experience working in financial services, banking, or fintech environments.
  • Familiarity with GitOps principles and tools like ArgoCD or Flux.
  • Familiarity with AI/ML services and integrations offered by major cloud providers (e.g., AWS SageMaker, Azure ML, Vertex AI) and the ability to support infrastructure for data science and machine learning workflows.
  • Experience with Google Cloud Platform (GCP) services such as Compute Engine, Cloud Functions, BigQuery, and GKE.
  • Exposure to multi-cloud or cloud-agnostic architectures, ensuring portability and resilience across AWS, Azure, and GCP.
  • Basic understanding of MLOps practices, pipelines, and model deployment lifecycle.

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