
Responsibilities: 1. Design, develop, and implement MLOps pipelines for the continuous deployment and integration of machine learning models 2. Collaborate with data scientists and engineers to understand model requirements and optimize deployment processes 3. Automate the training, testing and deployment processes for machine learning models 4. Continuously monitor and maintain models in production, ensuring optimal performance, accuracy and reliability 5. Implement best practices for version control, model reproducibility and governance 6. Optimize machine learning pipelines for scalability, efficiency and cost-effectiveness 7. Troubleshoot and resolve issues related to model deployment and performance 8. Ensure compliance with security and data privacy standards in all MLOps activities 9. Keep up to date with the latest MLOps tools, technologies and trends 10. Provide support and guidance to other team members on MLOps practices
Required skills and experience: • 3-10 years of experience in MLOps, DevOps or a related field • Bachelor’s degree in computer science, Data Science or a related field • Strong understanding of machine learning principles and model lifecycle management • Experience in Jenkins pipeline development • Experience in automation scripting

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Required Skills: Advanced AWS Infrastructure Expertise, CI/CD Pipeline Automation, Monitoring, Observability & Incident Management, Security, Networking & Risk Management, Infrastructure as Code & Scripting
Criteria:
- 5+ years of DevOps/SRE experience in cloud-native, product-based companies (B2C scale preferred)
- Strong hands-on AWS expertise across core and advanced services (EC2, ECS/EKS, Lambda, S3, CloudFront, RDS, VPC, IAM, ELB/ALB, Route53)
- Proven experience designing high-availability, fault-tolerant cloud architectures for large-scale traffic
- Strong experience building & maintaining CI/CD pipelines (Jenkins mandatory; GitHub Actions/GitLab CI a plus)
- Prior experience running production-grade microservices deployments and automated rollout strategies (Blue/Green, Canary)
- Hands-on experience with monitoring & observability tools (Grafana, Prometheus, ELK, CloudWatch, New Relic, etc.)
- Solid hands-on experience with MongoDB in production, including performance tuning, indexing & replication
- Strong scripting skills (Bash, Shell, Python) for automation
- Hands-on experience with IaC (Terraform, CloudFormation, or Ansible)
- Deep understanding of networking fundamentals (VPC, subnets, routing, NAT, security groups)
- Strong experience in incident management, root cause analysis & production firefighting
Description
Role Overview
Company is seeking an experienced Senior DevOps Engineer to design, build, and optimize cloud infrastructure on AWS, automate CI/CD pipelines, implement monitoring and security frameworks, and proactively identify scalability challenges. This role requires someone who has hands-on experience running infrastructure at B2C product scale, ideally in media/OTT or high-traffic applications.
Key Responsibilities
1. Cloud Infrastructure — AWS (Primary Focus)
- Architect, deploy, and manage scalable infrastructure using AWS services such as EC2, ECS/EKS, Lambda, S3, CloudFront, RDS, ELB/ALB, VPC, IAM, Route53, etc.
- Optimize cloud cost, resource utilization, and performance across environments.
- Design high-availability, fault-tolerant systems for streaming workloads.
2. CI/CD Automation
- Build and maintain CI/CD pipelines using Jenkins, GitHub Actions, or GitLab CI.
- Automate deployments for microservices, mobile apps, and backend APIs.
- Implement blue/green and canary deployments for seamless production rollouts.
3. Observability & Monitoring
- Implement logging, metrics, and alerting using tools like Grafana, Prometheus, ELK, CloudWatch, New Relic, etc.
- Perform proactive performance analysis to minimize downtime and bottlenecks.
- Set up dashboards for real-time visibility into system health and user traffic spikes.
4. Security, Compliance & Risk Highlighting
• Conduct frequent risk assessments and identify vulnerabilities in:
o Cloud architecture
o Access policies (IAM)
o Secrets & key management
o Data flows & network exposure
• Implement security best practices including VPC isolation, WAF rules, firewall policies, and SSL/TLS management.
5. Scalability & Reliability Engineering
- Analyze traffic patterns for OTT-specific load variations (weekends, new releases, peak hours).
- Identify scalability gaps and propose solutions across:
- o Microservices
- o Caching layers
- o CDN distribution (CloudFront)
- o Database workloads
- Perform capacity planning and load testing to ensure readiness for 10x traffic growth.
6. Database & Storage Support
- Administer and optimize MongoDB for high-read/low-latency use cases.
- Design backup, recovery, and data replication strategies.
- Work closely with backend teams to tune query performance and indexing.
7. Automation & Infrastructure as Code
- Implement IaC using Terraform, CloudFormation, or Ansible.
- Automate repetitive infrastructure tasks to ensure consistency across environments.
Required Skills & Experience
Technical Must-Haves
- 5+ years of DevOps/SRE experience in cloud-native, product-based companies.
- Strong hands-on experience with AWS (core and advanced services).
- Expertise in Jenkins CI/CD pipelines.
- Solid background working with MongoDB in production environments.
- Good understanding of networking: VPCs, subnets, security groups, NAT, routing.
- Strong scripting experience (Bash, Python, Shell).
- Experience handling risk identification, root cause analysis, and incident management.
Nice to Have
- Experience with OTT, video streaming, media, or any content-heavy product environments.
- Familiarity with containers (Docker), orchestration (Kubernetes/EKS), and service mesh.
- Understanding of CDN, caching, and streaming pipelines.
Personality & Mindset
- Strong sense of ownership and urgency—DevOps is mission critical at OTT scale.
- Proactive problem solver with ability to think about long-term scalability.
- Comfortable working with cross-functional engineering teams.
Why Join company?
• Build and operate infrastructure powering millions of monthly users.
• Opportunity to shape DevOps culture and cloud architecture from the ground up.
• High-impact role in a fast-scaling Indian OTT product.
Role: Full-Time, Long-Term Required: Docker, GCP, CI/CD Preferred: Experience with ML pipelines
OVERVIEW
We are seeking a DevOps engineer to join as a core member of our technical team. This is a long-term position for someone who wants to own infrastructure and deployment for a production machine learning system. You will ensure our prediction pipeline runs reliably, deploys smoothly, and scales as needed.
The ideal candidate thinks about failure modes obsessively, automates everything possible, and builds systems that run without constant attention.
CORE TECHNICAL REQUIREMENTS
Docker (Required): Deep experience with containerization. Efficient Dockerfiles, layer caching, multi-stage builds, debugging container issues. Experience with Docker Compose for local development.
Google Cloud Platform (Required): Strong GCP experience: Cloud Run for serverless containers, Compute Engine for VMs, Artifact Registry for images, Cloud Storage, IAM. You can navigate the console but prefer scripting everything.
CI/CD (Required): Build and maintain deployment pipelines. GitHub Actions required. You automate testing, building, pushing, and deploying. You understand the difference between continuous integration and continuous deployment.
Linux Administration (Required): Comfortable on the command line. SSH, diagnose problems, manage services, read logs, fix things. Bash scripting is second nature.
PostgreSQL (Required): Database administration basics—backups, monitoring, connection management, basic performance tuning. Not a DBA, but comfortable keeping a production database healthy.
Infrastructure as Code (Preferred): Terraform, Pulumi, or similar. Infrastructure should be versioned, reviewed, and reproducible—not clicked together in a console.
WHAT YOU WILL OWN
Deployment Pipeline: Maintaining and improving deployment scripts and CI/CD workflows. Code moves from commit to production reliably with appropriate testing gates.
Cloud Run Services: Managing deployments for model fitting, data cleansing, and signal discovery services. Monitor health, optimize cold starts, handle scaling.
VM Infrastructure: PostgreSQL and Streamlit on GCP VMs. Instance management, updates, backups, security.
Container Registry: Managing images in GitHub Container Registry and Google Artifact Registry. Cleanup policies, versioning, access control.
Monitoring and Alerting: Building observability. Logging, metrics, health checks, alerting. Know when things break before users tell us.
Environment Management: Configuration across local and production. Secrets management. Environment parity where it matters.
WHAT SUCCESS LOOKS LIKE
Deployments are boring—no drama, no surprises. Systems recover automatically from transient failures. Engineers deploy with confidence. Infrastructure changes are versioned and reproducible. Costs are reasonable and resources scale appropriately.
ENGINEERING STANDARDS
Automation First: If you do something twice, automate it. Manual processes are bugs waiting to happen.
Documentation: Runbooks, architecture diagrams, deployment guides. The next person can understand and operate the system.
Security Mindset: Secrets never in code. Least-privilege access. You think about attack surfaces.
Reliability Focus: Design for failure. Backups are tested. Recovery procedures exist and work.
CURRENT ENVIRONMENT
GCP (Cloud Run, Compute Engine, Artifact Registry, Cloud Storage), Docker, Docker Compose, GitHub Actions, PostgreSQL 16, Bash deployment scripts with Python wrapper.
WHAT WE ARE LOOKING FOR
Ownership Mentality: You see a problem, you fix it. You do not wait for assignment.
Calm Under Pressure: When production breaks, you diagnose methodically.
Communication: You explain infrastructure decisions to non-infrastructure people. You document what you build.
Long-Term Thinking: You build systems maintained for years, not quick fixes creating tech debt.
EDUCATION
University degree in Computer Science, Engineering, or related field preferred. Equivalent demonstrated expertise also considered.
TO APPLY
Include: (1) CV/resume, (2) Brief description of infrastructure you built or maintained, (3) Links to relevant work if available, (4) Availability and timezone.
Greetings!
Wissen Technology is hiring for Kubernetes Lead/Admin.
Required:
- 7+ years of relevant experience in Kubernetes
- Must have hands on experience on Implementation, CI/CD pipeline, EKS architecture, ArgoCD & Statefulset services.
- Good to have exposure on scripting languages
- Should be open to work from Chennai
- Work mode will be Hybrid
Company profile:
Company Name : Wissen Technology
Group of companies in India : Wissen Technology & Wissen Infotech
Work Location - Bangalore
Website : www.wissen.com
Wissen Thought leadership : https://www.wissen.com/articles/
LinkedIn: https://www.linkedin.com/company/wissen-technology
We are hiring for a Lead DevOps Engineer in Cloud domain with hands on experience in Azure / GCP.
- Expertise in managing Cloud / VMWare resources and good exposure on Dockers/Kubernetes
- Working knowledge of operating systems( Unix, Linux, IBM AIX)
- Experience in installation, configuration and managing apache webserver, Tomcat/Jboss
- Good understanding of JVM, troubleshooting and performance tuning through thread dump and log analysis
-Strong expertise in Dev Ops tools:
- Deployment (Chef/Puppet/Ansible /Nebula/Nolio)
- SCM (TFS, GIT, ClearCase)
- Build tools (Ant,Maven, Make, Gradle)
- Artifact repositories (Nexes, JFrog ArtiFactory)
- CI tools (Jenkins, TeamCity),
- Experienced in scripting languages: Python, Ant, Bash and Shell
What will be required of you?
- Responsible for implementation and support of application/web server infrastructure for complex business applications
- Server configuration management, release management, deployments, automation & troubleshooting
- Set-up and configure Development, Staging, UAT and Production server environment for projects and install/configure all dependencies using the industry best practices
- Manage Code Repositories
- Manage, Document, Control and Innovate Development and Release procedure.
- Configure automated deployment on multiple environment
- Hands-on working experience of Azure or GCP.
- Knowledge Transfer the implementation to support team and until such time support any production issues
The candidate must have 2-3 years of experience in the domain. The responsibilities include:
● Deploying system on Linux-based environment using Docker
● Manage & maintain the production environment
● Deploy updates and fixes
● Provide Level 1 technical support
● Build tools to reduce occurrences of errors and improve customer experience
● Develop software to integrate with internal back-end systems
● Perform root cause analysis for production errors
● Investigate and resolve technical issues
● Develop scripts to automate visualization
● Design procedures for system troubleshooting and maintenance
● Experience working on Linux-based infrastructure
● Excellent understanding of MERN Stack, Docker & Nginx (Good to have Node Js)
● Configuration and managing databases such as Mongo
● Excellent troubleshooting
● Experience of working with AWS/Azure/GCP
● Working knowledge of various tools, open-source technologies, and cloud services
● Awareness of critical concepts in DevOps and Agile principles
● Experience of CI/CD Pipeline
As a MLOps Engineer in QuantumBlack you will:
Develop and deploy technology that enables data scientists and data engineers to build, productionize and deploy machine learning models following best practices. Work to set the standards for SWE and
DevOps practices within multi-disciplinary delivery teams
Choose and use the right cloud services, DevOps tooling and ML tooling for the team to be able to produce high-quality code that allows your team to release to production.
Build modern, scalable, and secure CI/CD pipelines to automate development and deployment
workflows used by data scientists (ML pipelines) and data engineers (Data pipelines)
Shape and support next generation technology that enables scaling ML products and platforms. Bring
expertise in cloud to enable ML use case development, including MLOps
Our Tech Stack-
We leverage AWS, Google Cloud, Azure, Databricks, Docker, Kubernetes, Argo, Airflow, Kedro, Python,
Terraform, GitHub actions, MLFlow, Node.JS, React, Typescript amongst others in our projects
Key Skills:
• Excellent hands-on expert knowledge of cloud platform infrastructure and administration
(Azure/AWS/GCP) with strong knowledge of cloud services integration, and cloud security
• Expertise setting up CI/CD processes, building and maintaining secure DevOps pipelines with at
least 2 major DevOps stacks (e.g., Azure DevOps, Gitlab, Argo)
• Experience with modern development methods and tooling: Containers (e.g., docker) and
container orchestration (K8s), CI/CD tools (e.g., Circle CI, Jenkins, GitHub actions, Azure
DevOps), version control (Git, GitHub, GitLab), orchestration/DAGs tools (e.g., Argo, Airflow,
Kubeflow)
• Hands-on coding skills Python 3 (e.g., API including automated testing frameworks and libraries
(e.g., pytest) and Infrastructure as Code (e.g., Terraform) and Kubernetes artifacts (e.g.,
deployments, operators, helm charts)
• Experience setting up at least one contemporary MLOps tooling (e.g., experiment tracking,
model governance, packaging, deployment, feature store)
• Practical knowledge delivering and maintaining production software such as APIs and cloud
infrastructure
• Knowledge of SQL (intermediate level or more preferred) and familiarity working with at least
one common RDBMS (MySQL, Postgres, SQL Server, Oracle)
Rules & Responsibilities:
- Design, implement and maintain all AWS infrastructure and services within a managed service environment
- Should be able to work on 24 X 7 shifts for support of infrastructure.
- Design, Deploy and maintain enterprise class security, network and systems management applications within an AWS environment
- Design and implement availability, scalability, and performance plans for the AWS managed service environment
- Continual re-evaluation of existing stack and infrastructure to maintain optimal performance, availability and security
- Manage the production deployment and deployment automation
- Implement process and quality improvements through task automation
- Institute infrastructure as code, security automation and automation or routine maintenance tasks
- Experience with containerization and orchestration tools like docker, Kubernetes
- Build, Deploy and Manage Kubernetes clusters thru automation
- Create and deliver knowledge sharing presentations and documentation for support teams
- Learning on the job and explore new technologies with little supervision
- Work effectively with onsite/offshore teams
Qualifications:
- Must have Bachelor's degree in Computer Science or related field and 4+ years of experience in IT
- Experience in designing, implementing, and maintaining all AWS infrastructure and services
- Design and implement availability, scalability, and performance plans for the AWS managed service environment
- Continual re-evaluation of existing stack and infrastructure to maintain optimal performance, availability, and security
- Hands-on technical expertise in Security Architecture, automation, integration, and deployment
- Familiarity with compliance & security standards across the enterprise IT landscape
- Extensive experience with Kubernetes and AWS(IAM, Route53, SSM, S3, EFS, EBS, ELB, Lambda, CloudWatch, CloudTrail, SQS, SNS, RDS, Cloud Formation, DynamoDB)
- Solid understanding of AWS IAM Roles and Policies
- Solid Linux experience with a focus on web (Apache Tomcat/Nginx)
- Experience with automation/configuration management using Terraform\Chef\Ansible or similar.
- Understanding of protocols/technologies like Microservices, HTTP/HTTPS, SSL/TLS, LDAP, JDBC, SQL, HTML
- Experience in managing and working with the offshore teams
- Familiarity with CI/CD systems such as Jenkins, GitLab CI
- Scripting experience (Python, Bash, etc.)
- AWS, Kubernetes Certification is preferred
- Ability to work with and influence Engineering teams
Must-Have’s:
- Hands-on DevOps (Git, Ansible, Terraform, Jenkins, Python/Ruby)
Job Description:
- Knowledge on what is a DevOps CI/CD Pipeline
- Understanding of version control systems like Git, including branching and merging strategies
- Knowledge of what is continuous delivery and integration tools like Jenkins, Github
- Knowledge developing code using Ruby or Python and Java or PHP
- Knowledge writing Unix Shell (bash, ksh) scripts
- Knowledge of what is automation/configuration management using Ansible, Terraform, Chef or Puppet
- Experience and willingness to keep learning in a Linux environment
- Ability to provide after-hours support as needed for emergency or urgent situations
Nice to have’s:
- Proficient with container based products like docker and Kubernetes
- Excellent communication skills (verbal and written)
- Able to work in a team and be a team player
- Knowledge of PHP, MySQL, Apache and other open source software
- BA/BS in computer science or similar









