Test Automation Engineer

We are seeking an experienced Lead DevOps Engineer with deep expertise in Kubernetes infrastructure design and implementation. This role requires someone who can architect, build, and manage enterprise-grade Kubernetes clusters from the ground up. You’ll lead modernization initiatives, shape infrastructure strategy, and work with cutting-edge cloud-native technologies.

🚀 Key Responsibilities

Infrastructure Design & Implementation

• Architect and design enterprise-grade Kubernetes clusters across AWS, Azure, and GCP.
• Build production-ready Kubernetes infrastructure with HA, scalability, and security best practices.
• Implement Infrastructure as Code with Terraform, Helm, and GitOps workflows.
• Set up monitoring, logging, and observability for Kubernetes workloads.
• Design and execute backup and disaster recovery strategies for containerized applications.

Leadership & Team Management

• Lead a team of 3–4 DevOps engineers, providing technical mentorship.
• Drive best practices in containerization, orchestration, and cloud-native development.
• Collaborate with development teams to optimize deployment strategies.
• Conduct code reviews and maintain infrastructure quality standards.
• Build knowledge-sharing culture with documentation and training.

Operational Excellence

• Manage and scale CI/CD pipelines integrated with Kubernetes.
• Implement security policies (RBAC, network policies, container scanning).
• Optimize cluster performance and cost-efficiency.
• Automate operations to minimize manual interventions.
• Ensure 99.9% uptime for production workloads.

Strategic Planning

• Define the infrastructure roadmap aligned with business needs.
• Evaluate and adopt new cloud-native technologies.
• Perform capacity planning and cloud cost optimization.
• Drive risk assessment and mitigation strategies.

🛠 Must-Have Technical Skills

Kubernetes Expertise

• 6+ years of hands-on Kubernetes experience in production.
• Deep knowledge of Kubernetes architecture (etcd, API server, scheduler, kubelet).
• Advanced Kubernetes networking (CNI, Ingress, Service mesh).
• Strong grasp of Kubernetes storage (CSI, PVs, StorageClasses).
• Experience with Operators and Custom Resource Definitions (CRDs).

Infrastructure as Code

• Terraform (advanced proficiency).
• Helm (developing and managing complex charts).
• Config management tools (Ansible, Chef, Puppet).
• GitOps workflows (ArgoCD, Flux).

Cloud Platforms

• Hands-on experience with at least 2 of the following:
• AWS: EKS, EC2, VPC, IAM, CloudFormation
• Azure: AKS, VNets, ARM templates
• GCP: GKE, Compute Engine, Deployment Manager

CI/CD & DevOps Tools

• Jenkins, GitLab CI, GitHub Actions, Azure DevOps
• Docker (advanced optimization and security practices)
• Container registries (ECR, ACR, GCR, Docker Hub)
• Strong Git workflows and branching strategies

Monitoring & Observability

• Prometheus & Grafana (metrics and dashboards)
• ELK/EFK stack (centralized logging)
• Jaeger/Zipkin (tracing)
• AlertManager (intelligent alerting)

💡 Good-to-Have Skills

• Service Mesh (Istio, Linkerd, Consul)
• Serverless (Knative, OpenFaaS, AWS Lambda)
• Running databases in Kubernetes (Postgres, MongoDB operators)
• ML pipelines (Kubeflow, MLflow)
• Security tools (Aqua, Twistlock, Falco, OPA)
• Compliance (SOC2, PCI-DSS, GDPR)
• Python/Go for automation
• Advanced Shell scripting (Bash/PowerShell)

🎓 Qualifications

• Bachelor’s in Computer Science, Engineering, or related field.
• Certifications (preferred):
• Certified Kubernetes Administrator (CKA)
• Certified Kubernetes Application Developer (CKAD)
• Cloud provider certifications (AWS/Azure/GCP).

Experience

• 6–7 years of DevOps/Infrastructure engineering.
• 4+ years of Kubernetes in production.
• 2+ years in a lead role managing teams.
• Experience with large-scale distributed systems and microservices.

Key Responsibilities:

1. Customer Outreach & Relationship Management:

• Make outbound calls to potential and existing clients to inform them about the company's services, products, and offers.
• Assist customers with inquiries related to trading, investment products, depository services, etc.
• Provide timely updates and information to clients about the stock market and their portfolio.
• Follow up with leads and ensure customer satisfaction, encouraging long-term client relationships.

1. Product Information and Advisory:

• Explain different financial products, including equity, commodities, mutual funds, and more.
• Help customers understand stock market trends, investment strategies, and opportunities to increase their portfolio value.

1. Lead Generation:

• Generate quality leads by identifying potential clients and following up with them to convert into active traders and investors.
• Maintain an accurate database of clients and prospects.

1. Sales Support:

• Assist in the execution of sales campaigns, including promotions and offers.
• Track client interactions, sales activities, and report progress regularly.

1. Problem Resolution:

• Address customer concerns, complaints, or issues related to trading accounts, transactions, or market activities.
• Ensure timely resolution to enhance customer satisfaction and trust.

1. Compliance and Reporting:

• Adhere to company policies, regulatory guidelines, and compliance procedures while making calls and handling client accounts.
• Maintain detailed records of interactions and sales activities.

Requirements:

• Educational Qualification: Graduate (preferably in Finance, Commerce, or related field).
• Experience: 1-2 years in a telecalling or customer support role, preferably within the financial services industry (stock broking, mutual funds, etc.).

Data Engineer - Python

Qube Research & Technologies (QRT) is a global quantitative and systematic investment manager, operating in all liquid asset classes across the world. We are a technology and data driven group implementing a scientific approach to investing. Combining data, research, technology and trading expertise has shaped QRT’s collaborative mindset which enables us to solve the most complex challenges. QRT’s culture of innovation continuously drives our ambition to deliver high quality returns for our investors.

Your future role within QRT

• Use Python web frameworks such as Dash or Streamlit to make latency data accessible to end users.
• Leverage OpenAI API to analyse and generate insights by using latency data and provide visualization interface to end users.
• Improve the Latency Data API to provide seamless access to latency data for end users, including researchers, support, and other teams within QRT.
• Optimize and fine-tune Large Language Models (LLMs) for use cases in natural language understanding, data processing, and predictive modelling. 

Your present skillset

• 4+ years' experience in a related field
• Advanced Python and OpenAI API proficiency.
• Good with LLM Concepts.
• Experience with large data sets, visualizations, and reports.
• High standards in code quality and development practices.
• Proficiency in managing structured/unstructured data.
• Expertise in scalable data systems.
• Strong AI/ML skills for pattern and anomaly detection.
• Experience with CI/CD processes and DevOps.
• Experience in the financial/trading sector.
• Familiarity with latency-sensitive systems and performance optimization

QRT is an equal opportunity employer. We welcome diversity as essential to our success. QRT empowers employees to work openly and respectfully to achieve collective success. In addition to professional achievement, we are offering initiatives and programs to enable employees achieve a healthy work-life balance.

• Sales Consultants:
• 
  
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