🎁 Before you apply, rehearse this interview. Create your free WorkMundi account and get an Interview Training on HelpsYouSpeak — no cost, no card. I want my training →
About Us Deccan AI Experts is a pioneering AI company founded by IIT Bombay and IIM Ahmedabad alumni, with a strong founding team from IITs, NITs, and BITS. We specialize in high-quality human-curated data, AI-first operations, and advanced AI evaluation systems. Our global network of technology experts helps train and evaluate next-generation AI models through expert engineering judgment and domain-specific expertise. About the Role We are seeking an ML Systems Engineer (Freelancer) to support advanced AI evaluation initiatives focused on machine learning infrastructure, distributed systems, model deployment, MLOps, large-scale AI systems, and AI-generated technical content evaluation. In this role, you will evaluate AI-generated machine learning architectures, deployment pipelines, distributed training workflows, inference systems, infrastructure code, system designs, and engineering documentation. Your expertise will help improve AI systems designed for production-scale machine learning, cloud infrastructure, model serving, and AI platform engineering. This position is ideal for professionals with experience in machine learning systems, backend engineering, MLOps, cloud computing, distributed systems, AI infrastructure, or platform engineering. Responsibilities - Review AI-generated infrastructure code, deployment workflows, architecture diagrams, CI/CD pipelines, technical documentation, and system design proposals. - Evaluate distributed machine learning systems involving GPU clusters, model orchestration, feature stores, experiment tracking, batch and real-time inference, and monitoring pipelines. - Verify AI-generated recommendations for model versioning, infrastructure scalability, fault tolerance, observability, latency optimization, and resource utilization. - Assess AI-generated code for maintainability, cloud best practices, infrastructure automation, and production readiness. - Identify architectural flaws, infrastructure bottlenecks, deployment risks, security vulnerabilities, and performance issues. - Provide structured feedback to improve AI performance in ML systems engineering, infrastructure design, and platform development. - Review peer-developed deliverables to maintain quality and consistency standards. Requirements - Bachelor's degree in Computer Science, Software Engineering, Artificial Intelligence, Machine Learning, Electrical Engineering, or a related field is required. - Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Distributed Systems, or a related discipline is preferred. - 3+ years of hands-on experience in ML systems engineering, MLOps, backend engineering, cloud infrastructure, distributed systems, or AI platform development. - Deploying and managing production machine learning systems. - Building scalable ML pipelines and inference services. - Developing infrastructure for model training, monitoring, and lifecycle management. - Reviewing AI-generated infrastructure code and architecture documentation. - Optimizing system performance, reliability, and scalability. - Proficiency in Python and one or more programming languages such as Go, Java, C++, Rust, or Scala . - Experience with technologies such as TensorFlow, PyTorch, MLflow, Kubeflow, Docker, Kubernetes, Ray, Spark, Airflow, Kafka, Redis, ONNX, Triton Inference Server, NVIDIA CUDA , or equivalent platforms. - Familiarity with cloud services including AWS, Microsoft Azure, Google Cloud Platform (GCP) , and DevOps tools such as Terraform, Helm, Prometheus, Grafana, GitHub Actions, Jenkins , or similar technologies. - Excellent analytical thinking, system design, problem-solving, and written English communication skills. - Strong attention to detail and ability to evaluate production-grade engineering solutions. - Ability to work independently in a remote, fast-paced environment. Preferred Qualifications - Experience working with AI startups, hyperscale cloud providers, enterprise AI teams, research laboratories, or technology companies deploying large-scale machine learning systems. - Expertise in one or more areas such as LLM infrastructure, Generative AI platforms, distributed GPU training, inference optimization, vector databases, retrieval-augmented generation (RAG), feature engineering platforms, or AI observability. - Experience evaluating AI-generated code, infrastructure documentation, deployment pipelines, architecture diagrams, or technical design documents. - Familiarity with Generative AI, prompt engineering, RLHF (Reinforcement Learning from Human Feedback), AI benchmarking, model evaluation, or AI safety is highly desirable. - Contributions to open-source ML infrastructure projects, conference presentations, technical blogs, patents, or research publications are a plus. - Professional certifications such as AWS Certified Machine Learning Specialty , Google Qualified Machine Learning Engineer , Azure AI Engineer Associate , Certified Kubernetes Administrator
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.