Data Engineer - (DevOps and ML) - Ahmedabad, India
AI in this role
Description
We are seeking an experienced DevOps / MLOps Engineer to support a large-scale data and AI platform initiative on Google Cloud Platform (GCP). This role is responsible for building and operating the CI/CD, infrastructure automation, and machine learning lifecycle capabilities that allow data science and engineering teams to move models and data pipelines from experimentation to production reliably, securely, and at scale. The role requires strong expertise in Azure DevOps, cloud infrastructure, containerization, automation, and ML model operations, along with the ability to deliver production-grade platforms aligned with business requirements and project timelines.
Key Responsibilities
- Design, build, and maintain CI/CD pipelines in Azure DevOps (YAML pipelines, Azure Repos, Azure Artifacts, service connections, environments, and approval gates) for data pipelines, ML models, and application services deployed to GCP.
- Provision and manage GCP infrastructure using Infrastructure as Code (Terraform) executed through Azure Pipelines, covering GKE, Cloud Run, BigQuery, Cloud Storage, Vertex AI, Composer, and networking/IAM.
- Build and operate end-to-end MLOps workflows on Vertex AI (or equivalent), including feature stores, training pipelines, model registry, automated evaluation, and deployment to batch and online endpoints, triggered and governed through Azure DevOps.
- Containerize and orchestrate workloads with Docker and Kubernetes (GKE), including Helm charts, autoscaling, and resource optimization for training and inference jobs.
- Implement model monitoring for drift, data quality, performance degradation, and cost, with automated alerting and retraining triggers.
- Establish reproducibility and governance practices: experiment tracking (MLflow / Vertex Experiments), data and model versioning, lineage, branching strategies, and promotion gates across dev, UAT, and production environments.
- Implement observability across platforms and services using Cloud Monitoring, Cloud Logging, Prometheus, and Grafana, and define SLOs and incident response processes.
- Embed security and compliance into the delivery lifecycle: secrets management (Azure Key Vault / GCP Secret Manager), IAM least privilege, vulnerability scanning, image signing, and policy-as-code within pipelines.
- Support migration of on-premises data and ML workloads to GCP, including redesign of build, deployment, and orchestration patterns where required.
- Optimize cloud cost and performance across compute, storage, and ML serving resources.
- Collaborate with data scientists, data engineers, and application teams to standardize pipeline templates, development environments, and deployment patterns; manage Azure Boards work items and release planning where applicable.
- Participate in UAT, release management, production support, and documentation of platform standards and runbooks.
Be part of an extraordinary story
Your skills. Your imagination. Your ambition. Here, there are no boundaries to your potential and the impact you can make. You’ll find infinite opportunities to grow and work on the biggest, most rewarding challenges that will build your skills and experience. You have the chance to be a part of our future, and build the life you want while being part of an international community. Our best is here and still to come. To us, impossible is only a challenge. Join us as we dare to achieve what’s never been done before.
Together, everything is possible
Qualification
- Strong hands-on expertise in:
- Azure DevOps (YAML pipelines, Azure Repos, Azure Artifacts, environments, approvals, service connections, pipeline templates)
- GCP services (GKE, Cloud Run, Vertex AI, BigQuery, Cloud Storage, Composer, IAM)
- Terraform and Infrastructure as Code practices
- Docker, Kubernetes, and Helm
- Proven experience deploying to GCP from Azure DevOps, including Workload Identity Federation or service account integration and multi-environment release strategies.
- Strong proficiency in Python and shell scripting; working knowledge of SQL.
- Proven experience operationalizing ML models, including pipeline orchestration (Vertex AI Pipelines, Kubeflow, or Airflow), model registry, and serving (batch and real-time).
- Solid understanding of ML lifecycle concepts: experiment tracking, feature engineering, model validation, drift monitoring, and retraining strategies.
- Experience with monitoring and logging stacks (Cloud Monitoring, Prometheus, Grafana, ELK or equivalent).
- Experience with DevSecOps practices: secrets management, container scanning, policy enforcement, and audit readiness.
- Proven experience in cloud migration projects, including:
- On-premise → GCP platform transition
- Migration of ML or data workloads to managed cloud services
- Familiarity with data engineering tools in the GCP ecosystem (Dataflow, Dataproc, Pub/Sub) is preferred.
- Google Cloud Professional certifications
- Exposure to Azure cloud services or multi-cloud environments
- Experience with LLM / GenAI deployment patterns (RAG, vector databases, model gateways)
- Experience with GitOps (Argo CD) and service mesh technologies
- Knowledge of aviation, travel, or large-enterprise regulated environments
- Ability to work closely with data science and business stakeholders to translate requirements into platform capabilities.
- Excellent communication and collaboration skills.
- Ability to manage multiple platforms, environments, and release scenarios effectively.
Our story started with four aircraft. Today, we deliver excellence across 12 different businesses coming together as one. We’ve grown fast, broken records and set trends that others follow. We don’t slow down by the fear of failure. Instead, we dare to achieve what’s never been done before. So whether you’re creating a unique experience for our customers or innvating behind the scenes, every person contributes to our proud story. A story of spectacular growth and determination. Now is the time to bring your best ideas and passion to a place where your ambition will know no boundaries, and be part of a truly global community
https://aa115.taleo.net/careersection/QA_External_CS/jobapply.ftl?lang=en&job=235693How we rate this
Data Engineer - (DevOps and ML) - Ahmedabad, India at Qatar Airways rates 87 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.
Prepare for this job
A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.
Skills and AI tools this role asks for
Questions you could be asked
- How would you design a retrieval step so the model answers from real data instead of guessing?
- How do you monitor a model once it's live, and how do you know it needs retraining?
- What are the limits of Vertex AI that you've run into, and how did you work around them?
- What's a project where you used Mlflow hands-on?
- How would you decide a model or AI system is ready to ship?
Adapt your resume
- List these exact terms on your resume: RAG, ML Ops, Vertex AI, and Mlflow. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.
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