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About The Role The role owns the infrastructure, orchestration, and continuous deployment pipelines that power machine learning at scale. The focus is on turning experimental AI prototypes into reliable, low-latency, production-ready microservices. The team works closely with applied scientists and data engineers to build robust platforms that handle model tracking, automated retraining, and infrastructure scaling across cloud environments. Key Responsibilities Architect, build, and maintain end-to-end MLOps pipelines using Kubernetes, Docker, and CI/CD tools like GitHub Actions or ArgoCD Deploy and manage model serving infrastructure leveraging Triton, Ray Serve, or vLLM to optimize throughput and inference latency Implement comprehensive model monitoring systems to detect data drift, concept drift, and system anomalies in production Automate model training and data preprocessing pipelines using orchestration tools such as Kubeflow, Airflow, or Prefect Collaborate with security and engineering teams to ensure governance, compliance, and reproducibility across all deployed ML artifacts Write clean, resilient infrastructure-as-code using Terraform and participate in on-call rotations for production systems What We Are Looking For 3–6 years of experience in MLOps, DevOps, or machine learning engineering with a strong focus on production infrastructure Deep expertise in containerization, Kubernetes orchestration, and cloud computing (AWS, GCP, or Azure) Hands-on experience with model serving frameworks, inference optimization techniques, and feature stores Proficiency in Python and Bash scripting, alongside experience building automated CI/CD pipelines Solid understanding of software engineering best practices, monitoring tools (Prometheus, Grafana), and logging infrastructure Bonus: Experience managing LLM infrastructure, fine-tuning pipelines, and vector database deployments at scale
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.