ML Systems Engineer (Freelancer)

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 Professional Machine Learning Engineer, Azure AI Engineer Associate, Certified Kubernetes Administrator (CKA), or equivalent certifications are advantageous.

Why Join Us

• Competitive hourly pay: ₹2,000/hour
• Fully remote with flexible working hours.
• Opportunity to contribute to cutting-edge AI initiatives in machine learning infrastructure, MLOps, and Generative AI.
• Exposure to advanced AI systems focused on distributed computing, scalable model deployment, cloud-native AI platforms, and production engineering.
• Flexible project-based opportunities with global teams.
• Work on next-generation AI solutions supporting technology companies, research organizations, cloud platforms, and enterprise AI systems.