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Key Responsibilities & Skillsets: - Design, build, and maintain scalable MLOps and LLMOps pipelines for deploying, monitoring, and managing machine learning and generative AI solutions in GCP. - Develop and operationalize end-to-end ML lifecycle workflows including data ingestion, feature engineering, model training, validation, deployment, and monitoring. - Build and manage LLMOps workflows for Large Language Models, including prompt management, RAG pipelines, vector databases, model evaluation, guardrails, and observability. - Deploy and manage ML and GenAI workloads using Vertex AI, GKE, Cloud Run, and other GCP-native services. - Implement CI/CD and CT pipelines for ML models and LLM-based applications using tools such as GitHub Actions, Cloud Build, Jenkins, or Terraform. - Collaborate with Data Scientists, ML Engineers, Data Engineers, and Product teams to productionize machine learning and GenAI use cases. - Establish model monitoring frameworks for drift detection, latency tracking, usage analytics, output quality, and operational performance. - Build reusable and scalable infrastructure for experimentation, model versioning, artifact tracking, and automated retraining. - Manage model registry, feature store integration, metadata tracking, and pipeline orchestration using modern MLOps tooling. - Implement secure and responsible AI practices including access control, governance, model auditability, and compliance with enterprise policies. - Optimize inference workloads for performance, cost, scalability, and reliability across batch and real-time serving environments. - Research and adopt best practices in MLOps, LLMOps, GenAI deployment, and GCP architecture to continuously improve platform capabilities. - Support debugging, troubleshooting, and incident resolution across ML platforms, deployment pipelines, and production workloads. - Document architecture, pipeline design, deployment processes, and operational standards for internal teams and stakeholders. Candidate Profile: - Bachelors or Masters degree in Computer Science, Data Engineering, Artificial Intelligence, or a related discipline. - 5 to 7 years of experience in Machine Learning Engineering, MLOps, ML Platform Engineering & LLMOps. - Robust hands-on experience in MLOps on GCP, especially with services such as Vertex AI, BigQuery, GCS, Cloud Functions, Cloud Run, GKE, Pub/Sub, and IAM. - Solid experience in building and managing LLMOps workflows, including RAG pipelines, vector databases, prompt orchestration, evaluation frameworks, and LLM observability. - Proficiency in Python, SQL, and scripting for automation and pipeline orchestration. - Strong knowledge of containerization and orchestration tools such as Docker and Kubernetes. - Experience with ML workflow orchestration and pipeline tools such as Kubeflow, Vertex AI Pipelines, Airflow, or similar frameworks. - Hands-on experience with CI/CD, Infrastructure as Code, and DevOps tools such as Terraform, GitHub Actions, Cloud Build, Jenkins, and Git. - Familiarity with model tracking, experiment management, and registry tools such as MLflow, Vertex AI Model Registry, or equivalent. - Good understanding of feature stores, model monitoring, drift detection, logging, and production support for ML systems. - Experience with LLM ecosystem tools and frameworks such as LangChain, LangGraph, LlamaIndex, Hugging Face, or similar is preferred. - Knowledge of vector databases such as Pinecone, Weaviate, Chroma, Vertex AI Vector Search, or equivalent is a plus. - Strong understanding of cloud security, IAM policies, secrets management, governance, and responsible AI practices. - Excellent problem-solving, communication, and stakeholder collaboration skills in cross-functional delivery environments. - Exposure to scalable AI/ML deployments in enterprise settings, especially real-time and high-availability systems, will be an added advantage. .
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.