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Opportunities For IBM MQ Engineer / Architect (Containerization & EKS

Tech Mahindra

  • Bengaluru, India
  • Competitive Package (Best in Industry)
  • 7-11 Yrs
  • Posted Today
HR
Recruiter contact In post
Recruiter / hiring contact
uk00753470@techmahindra.com at Tech Mahindra

About the role

Role & responsibilities Seeking an experienced IBM MQ Engineer / Architect with strong containerization and Kubernetes knowledge to lead the migration of IBM MQ workloads from Amazon EC2 to Amazon EKS. The role will own assessment, target-state design, migration planning, build, testing, cutover, and post-migration stabilization for a secure, highly available, and observable IBM MQ platform running on Kubernetes. Preferred candidate profile Assess the current IBM MQ estate running on EC2, including queue managers, channels, clustering, HA/DR setup, storage, security, monitoring, and dependencies. Define the target architecture for IBM MQ on EKS, including container strategy, namespace design, networking, persistent storage, secrets handling, and high availability. Design and implement IBM MQ containerization patterns using Docker/OCI images and Kubernetes-native deployment approaches. Build and manage deployment automation for IBM MQ on EKS using Helm, Kubernetes manifests, CI/CD pipelines, and infrastructure-as-code where applicable. Plan and execute migration from EC2 to EKS with minimal disruption, including environment setup, data persistence strategy, queue manager migration, rollback planning, and cutover support. Configure persistent volumes, storage classes, backup, restore, and disaster recovery for stateful IBM MQ workloads on Kubernetes. Implement security controls including TLS, certificate management, IAM integration, secrets management, encryption, and platform hardening. Partner with application, middleware, infrastructure, cloud, and security teams to validate connectivity, performance, and production readiness. Establish monitoring, alerting, logging, and observability for IBM MQ on EKS using enterprise tooling. Tune performance, capacity, and resilience characteristics for containerized MQ workloads. Produce architecture documents, runbooks, SOPs, migration plans, and operational handover materials. Support hypercare and post-production stabilization after migration. Required Skills 7+ years of hands-on experience with IBM MQ administration and engineering. Strong experience with IBM MQ queue managers, channels, listeners, clustering, client connectivity, SSL/TLS, and troubleshooting. Hands-on experience containerizing middleware platforms, ideally IBM MQ. Strong knowledge of Kubernetes and Amazon EKS, including pods, StatefulSets, services, ingress/network policies, volumes, config maps, secrets, and RBAC. Experience migrating stateful enterprise workloads from virtual machines or EC2 to Kubernetes. Strong understanding of AWS infrastructure, including EC2, EKS, VPC, IAM, EBS/EFS, load balancing, Route 53, CloudWatch, and security controls. Experience with CI/CD and automation tools such as Jenkins, GitLab, GitHub Actions, Argo CD, Terraform, Ansible, or similar. Knowledge of Linux administration, shell scripting, and container runtime concepts. Experience with monitoring and logging platforms such as Splunk, ELK, Prometheus, Grafana, Dynatrace, or AppDynamics. Strong troubleshooting skills across middleware, OS, networking, storage, and cloud layers. Preferred Skills Experience with IBM MQ Operator or similar Kubernetes-native operational models. Prior experience designing HA/DR for IBM MQ on Kubernetes. Experience with service mesh, certificate automation, and secrets platforms such as HashiCorp Vault. Familiarity with enterprise integration patterns involving IBM MQ, APIs, microservices, and event-driven systems. Exposure to regulated environments with strong audit, security, and resiliency requirements. Experience with OpenShift is a plus. Looking Only immediate joiner if interested pls share resume on uk00753470@techmahindra.com

Eligibility & process

Who can apply

Experience: 7-11 Yrs. Open to matching qualifications.

Skills & stack

Mq Series MQ Eks Containerization Architecture Microservices AWS Docker

Selection process

  1. Online Assessment / Technical Interview Rounds / HR Review

Prep kit

19 resources
Study material 4
Company-specific guides 4
Likely coding questions 4
Round-wise strategy 4
Round 1: Online Assessment (OA) Aptitude, Quantitative Logic & 2 Coding Problems Focus on accuracy and speed. Practice arrays, strings, and standard arithmetic puzzles.
Round 2: Technical Interview 1 Core Tech Stack (Mq Series, MQ, Eks, Containerization, Architecture, Microservices, AWS, Docker) & Live Code Tracing Explain your thought process aloud. Analyze time & space complexities before coding.
Round 3: System Design & Problem Solving Database Schemas, APIs & Architecture Basics Clarify edge cases, diagram schemas cleanly, and discuss scalability trade-offs.
Round 4: HR & Cultural Fit Discussion Tech Mahindra Core Values, Learning Agility & Offer Terms Demonstrate passion, strong communication, and readiness for full-time collaboration.
Technical questions 4
What is the bias-variance tradeoff and how do you prevent overfitting? Answer
Model answer: High bias leads to underfitting (oversimplified model), high variance leads to overfitting (captures noise). Mitigate using L1/L2 Regularization, Dropout, Cross-Validation, and data augmentation.
Explain the difference between Precision, Recall, and F1-Score. Answer
Model answer: Precision = TP / (TP + FP) (correctness of positive predictions). Recall = TP / (TP + FN) (coverage of actual positives). F1-Score is the harmonic mean of Precision and Recall.
How does Gradient Descent work and what is the role of Learning Rate? Answer
Model answer: It optimizes loss functions by iteratively moving weights in the direction of negative gradient. A large learning rate may overshoot the minimum; a small rate causes slow convergence.
What is the difference between Supervised, Unsupervised, and Self-Supervised learning? Answer
Model answer: Supervised uses labeled data (X -> y). Unsupervised finds hidden patterns in unlabeled data (clustering/PCA). Self-supervised generates labels from input data (e.g. masked language modeling in BERT/Transformers).
Behavioural & HR questions 3
Why do you want to join Tech Mahindra as a Opportunities For IBM MQ Engineer / Architect (Containerization & EKS?
Highlight Tech Mahindra's market reputation, recent tech innovations, and how your skills in Mq Series directly solve their team's objectives.
Describe a challenging bug or academic project roadblock and how you resolved it.
Use the STAR method: Situation (project context), Task (what needed solving), Action (specific tools/logic applied), Result (quantifiable positive outcome).
How do you handle strict deadlines or sudden scope changes?
Explain your prioritization strategy, proactive communication with mentors/peers, and agile mindset.
Tech Mahindra Competitive Package (Best in Industry)
Recruiter Resume Apply
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