11+ OPA Jobs in India

ROLE & RESPONSIBILITIES:

We are hiring a Senior DevSecOps / Security Engineer with 8+ years of experience securing AWS cloud, on-prem infrastructure, DevOps platforms, MLOps environments, CI/CD pipelines, container orchestration, and data/ML platforms. This role is responsible for creating and maintaining a unified security posture across all systems used by DevOps and MLOps teams — including AWS, Kubernetes, EMR, MWAA, Spark, Docker, GitOps, observability tools, and network infrastructure.

KEY RESPONSIBILITIES:

1.     Cloud Security (AWS)-

• Secure all AWS resources consumed by DevOps/MLOps/Data Science: EC2, EKS, ECS, EMR, MWAA, S3, RDS, Redshift, Lambda, CloudFront, Glue, Athena, Kinesis, Transit Gateway, VPC Peering.
• Implement IAM least privilege, SCPs, KMS, Secrets Manager, SSO & identity governance.
• Configure AWS-native security: WAF, Shield, GuardDuty, Inspector, Macie, CloudTrail, Config, Security Hub.
• Harden VPC architecture, subnets, routing, SG/NACLs, multi-account environments.
• Ensure encryption of data at rest/in transit across all cloud services.

2.     DevOps Security (IaC, CI/CD, Kubernetes, Linux)-

Infrastructure as Code & Automation Security:

• Secure Terraform, CloudFormation, Ansible with policy-as-code (OPA, Checkov, tfsec).
• Enforce misconfiguration scanning and automated remediation.

CI/CD Security:

• Secure Jenkins, GitHub, GitLab pipelines with SAST, DAST, SCA, secrets scanning, image scanning.
• Implement secure build, artifact signing, and deployment workflows.

Containers & Kubernetes:

• Harden Docker images, private registries, runtime policies.
• Enforce EKS security: RBAC, IRSA, PSP/PSS, network policies, runtime monitoring.
• Apply CIS Benchmarks for Kubernetes and Linux.

Monitoring & Reliability:

• Secure observability stack: Grafana, CloudWatch, logging, alerting, anomaly detection.
• Ensure audit logging across cloud/platform layers.

3.     MLOps Security (Airflow, EMR, Spark, Data Platforms, ML Pipelines)-

Pipeline & Workflow Security:

• Secure Airflow/MWAA connections, secrets, DAGs, execution environments.
• Harden EMR, Spark jobs, Glue jobs, IAM roles, S3 buckets, encryption, and access policies.

ML Platform Security:

• Secure Jupyter/JupyterHub environments, containerized ML workspaces, and experiment tracking systems.
• Control model access, artifact protection, model registry security, and ML metadata integrity.

Data Security:

• Secure ETL/ML data flows across S3, Redshift, RDS, Glue, Kinesis.
• Enforce data versioning security, lineage tracking, PII protection, and access governance.

ML Observability:

• Implement drift detection (data drift/model drift), feature monitoring, audit logging.
• Integrate ML monitoring with Grafana/Prometheus/CloudWatch.

4.     Network & Endpoint Security-

• Manage firewall policies, VPN, IDS/IPS, endpoint protection, secure LAN/WAN, Zero Trust principles.
• Conduct vulnerability assessments, penetration test coordination, and network segmentation.
• Secure remote workforce connectivity and internal office networks.

5.     Threat Detection, Incident Response & Compliance-

• Centralize log management (CloudWatch, OpenSearch/ELK, SIEM).
• Build security alerts, automated threat detection, and incident workflows.
• Lead incident containment, forensics, RCA, and remediation.
• Ensure compliance with ISO 27001, SOC 2, GDPR, HIPAA (as applicable).
• Maintain security policies, procedures, RRPs (Runbooks), and audits.

IDEAL CANDIDATE:

• 8+ years in DevSecOps, Cloud Security, Platform Security, or equivalent.
• Proven ability securing AWS cloud ecosystems (IAM, EKS, EMR, MWAA, VPC, WAF, GuardDuty, KMS, Inspector, Macie).
• Strong hands-on experience with Docker, Kubernetes (EKS), CI/CD tools, and Infrastructure-as-Code.
• Experience securing ML platforms, data pipelines, and MLOps systems (Airflow/MWAA, Spark/EMR).
• Strong Linux security (CIS hardening, auditing, intrusion detection).
• Proficiency in Python, Bash, and automation/scripting.
• Excellent knowledge of SIEM, observability, threat detection, monitoring systems.
• Understanding of microservices, API security, serverless security.
• Strong understanding of vulnerability management, penetration testing practices, and remediation plans.

EDUCATION:

• Master’s degree in Cybersecurity, Computer Science, Information Technology, or related field.
• Relevant certifications (AWS Security Specialty, CISSP, CEH, CKA/CKS) are a plus.

PERKS, BENEFITS AND WORK CULTURE:

• Competitive Salary Package
• Generous Leave Policy
• Flexible Working Hours
• Performance-Based Bonuses
• Health Care Benefits

We are seeking a highly experienced Azure AI, AIOps & MLOps Architect to lead enterprise-scale AI platform engineering, cloud modernization, DevSecOps transformation, and intelligent automation initiatives.

The ideal candidate should possess deep expertise in Microsoft Azure, Azure AI Foundry, Azure OpenAI, Azure Machine Learning, Kubernetes, Terraform, Azure DevOps, and enterprise observability platforms. The role will focus on designing scalable AI platforms, implementing MLOps and AIOps capabilities, enabling Agentic AI architectures, and driving cloud-native engineering practices across the organization.

Key Responsibilities

Cloud Architecture & Engineering

• Design and implement scalable, secure, and highly available solutions on Microsoft Azure.

• Define cloud architecture standards, reference architectures, and best practices.

• Lead cloud migration and modernisation initiatives across enterprise workloads.

• Implement multi-region disaster recovery and business continuity strategies.

• Oversee Azure networking, identity, security, and governance frameworks.

DevOps & CI/CD

• Architect and implement end-to-end CI/CD pipelines using Azure DevOps or GitHub Actions.

• Drive DevSecOps culture — embedding security scanning, quality gates, and compliance into the delivery pipeline.

• Champion Infrastructure-as-Code (IaC) practices using Terraform, Bicep, or ARM templates.

• Establish branching strategies, release management, and environment promotion standards.

• Define and enforce platform engineering standards and internal developer tooling.

AI & Machine Learning Integration

• Architect AI/ML solutions leveraging Azure AI services — Azure OpenAI, Azure Machine Learning, Azure AI Foundry, and Cognitive Services.

• Design intelligent automation and agentic workflows integrated into enterprise DevOps processes.

• Implement AI-powered capabilities such as code review assistance, anomaly detection, predictive analytics, and natural language automation.

• Define AI governance frameworks: model evaluation, prompt management, responsible AI, and cost controls.

• Design and implement enterprise MLOps frameworks.

• Build automated model training, validation, deployment, and monitoring pipelines.

• Establish model governance and lifecycle management.

Generative AI & Agentic AI

• Design enterprise GenAI solutions using Azure OpenAI.

• Build AI Agents using Azure AI Foundry.

• Develop Agent-to-Agent communication patterns.

• Implement Retrieval Augmented Generation (RAG) architectures.

• Build enterprise Knowledge Management and AI Skill Registry platforms.

• Design multi-agent orchestration frameworks.

Leadership & Stakeholder Engagement

• Serve as the technical authority and subject matter expert for Azure AI and DevOps practices.

• Mentor and guide junior architects, developers, and DevOps engineers.

• Collaborate with business stakeholders, product owners, and vendors to translate requirements into technical solutions.

• Produce architecture documentation, decision records (ADRs), and roadmaps.

• Represent the technology function in enterprise architecture forums and governance boards.

Required Qualifications

• Bachelor's or Master's degree in Computer Science, Information Technology, or a related field.

• 10+ years of experience in cloud engineering and architecture.

• 5+ years of hands-on experience with Microsoft Azure across compute, networking, storage, identity, and data services.

• Proven experience designing and implementing enterprise-grade CI/CD pipelines.

• Strong hands-on expertise with Infrastructure-as-Code (Terraform, Bicep, or ARM).

• Demonstrated experience architecting and deploying AI/ML solutions on Azure (Azure OpenAI, Azure ML, AI Foundry).

• Deep knowledge of DevSecOps principles, tools, and practices.

• Experience with containerisation and orchestration: Docker, Kubernetes (AKS).

• Proficiency in scripting and development: Python, PowerShell, Bash.

• Excellent communication and stakeholder management skills.

Preferred Qualifications

• Microsoft Certified: Azure Solutions Architect Expert.

• Microsoft Certified: DevOps Engineer Expert.

• Microsoft Certified: Azure AI Engineer Associate.

• Experience with Azure API Management (APIM), Event Grid, and Azure Functions.

• Familiarity with Datadog, Prometheus, or equivalent observability platforms.

• Experience in the real estate, retail, or enterprise industry sector.

• Knowledge of agentic AI frameworks and LLM orchestration patterns (LangChain, Semantic Kernel, MCP).

• Background in building Internal Developer Platforms (IDP).

We seek a skilled and motivated Azure DevOps engineer to join our dynamic team. The ideal candidate will design, implement, and manage CI/CD pipelines, automate deployments, and optimize cloud infrastructure using Azure DevOps tools and services. You will collaborate closely with development and IT teams to ensure seamless integration and delivery of software solutions in a fast-paced environment.

Responsibilities:

• Design, implement, and manage CI/CD pipelines using Azure DevOps.
• Automate infrastructure provisioning and deployments using Infrastructure as Code (IaC) tools like Terraform, ARM templates, or Azure CLI.
• Monitor and optimize Azure environments to ensure high availability, performance, and security.
• Collaborate with development, QA, and IT teams to streamline the software development lifecycle (SDLC).
• Troubleshoot and resolve issues related to build, deployment, and infrastructure.
• Implement and manage version control systems, primarily using Git.
• Manage containerization and orchestration using tools like Docker and Kubernetes.
• Ensure compliance with industry standards and best practices for security, scalability, and reliability.

Job Title: Senior AIML Engineer – Immediate Joiner (AdTech)

Location: Pune – Onsite

About Us:

We are a cutting-edge technology company at the forefront of digital transformation, building innovative AI and machine learning solutions for the digital advertising industry. Join us in shaping the future of AdTech!

Role Overview:

We are looking for a highly skilled Senior AIML Engineer with AdTech experience to develop intelligent algorithms and predictive models that optimize digital advertising performance. Immediate joiners preferred.

Key Responsibilities:

• Design and implement AIML models for real-time ad optimization, audience targeting, and campaign performance analysis.
• Collaborate with data scientists and engineers to build scalable AI-driven solutions.
• Analyze large volumes of data to extract meaningful insights and improve ad performance.
• Develop and deploy machine learning pipelines for automated decision-making.
• Stay updated on the latest AI/ML trends and technologies to drive continuous innovation.
• Optimize existing models for speed, scalability, and accuracy.
• Work closely with product managers to align AI solutions with business goals.

Requirements:

• Minimum 4-6 years of experience in AIML, with a focus on AdTech (Mandatory).
• Strong programming skills in Python, R, or similar languages.
• Hands-on experience with machine learning frameworks like TensorFlow, PyTorch, or Scikit-learn.
• Expertise in data processing and real-time analytics.
• Strong understanding of digital advertising, programmatic platforms, and ad server technology.
• Excellent problem-solving and analytical skills.
• Immediate joiners preferred.

Preferred Skills:

• Knowledge of big data technologies like Spark, Hadoop, or Kafka.
• Experience with cloud platforms like AWS, GCP, or Azure.
• Familiarity with MLOps practices and tools.

How to Apply:

If you are a passionate AIML engineer with AdTech experience and can join immediately, we want to hear from you. Share your resume and a brief note on your relevant experience.

Join us in building the future of AI-driven digital advertising!

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.

Must Have skills :

Experience in Linux Administration

Experience in building, deploying, and monitoring distributed apps using container systems (Docker) and container orchestration (Kubernetes, EKS)

Ability to read and understand code (Java / Python / R / Scala)

Experience AWS and tools

Nice to have skills:

Experience in SAS Viya administration

Experience managing large Big Data clusters

Experience in Big Data tools like Hue, Hive, Spark, Jupyter, SAS and R-Studio

DevOps Architect 

Experience:  10 - 12+ year relevant experience on DevOps
Locations : Bangalore, Chennai, Pune, Hyderabad, Jaipur.

Qualification:
• Bachelors or advanced degree in Computer science, Software engineering or equivalent is required.
• Certifications in specific areas are desired

Technical Skillset: Skills Proficiency level

• Build tools (Ant or Maven) - Expert
• CI/CD tool (Jenkins or Github CI/CD) - Expert
• Cloud DevOps (AWS CodeBuild, CodeDeploy, Code Pipeline etc) or Azure DevOps. - Expert
• Infrastructure As Code (Terraform, Helm charts etc.) - Expert
• Containerization (Docker, Docker Registry) - Expert
• Scripting (linux) - Expert
• Cluster deployment (Kubernetes) & maintenance - Expert
• Programming (Java) - Intermediate
• Application Types for DevOps (Streaming like Spark, Kafka, Big data like Hadoop etc) - Expert
• Artifactory (JFrog) - Expert
• Monitoring & Reporting (Prometheus, Grafana, PagerDuty etc.) - Expert
• Ansible, MySQL, PostgreSQL - Intermediate

Araali Networks is seeking a highly driven DevOps engineer to help streamline the release and deployment process, while assuring high quality.

Responsibilities:

Use best practices to streamline the release process

Use IaC methods to manage cloud infrastructure

Use test automation for release qualification

Skills and Qualifications:

Hands-on experience in managing release pipelines

Good understanding of public cloud infrastructure

Working knowledge of python test frameworks like pyunit, and automation tools like Selenium, Cucumber etc

Strong analytical and debugging skills

Bachelor’s degree in Computer Science

About Araali:

Araali Networks is a SaaS based cybersecurity startup that has raised a total of $10M from well known investors like A Capital, Firebolt Ventures and SV Angels, and and through a strategic investment by a publicly traded security company.

The company is disrupting the cloud firewall market by auto-creating nano-perimeters around every cloud app. The Araali solution enables developers to focus on features and improves the security posture through simplification and automation. 

The security controls are embedded at the time of DevOps. The precision of Araali controls also helps with security operations where alerts are precise and intelligently routed to the right app team, making it actionable in real-time.