Machine Learning Engineer

Responsibilities

• Design, develop, and maintain high-performance distributed systems to support large-scale machine learning inference and data processing.
• Build and optimize scalable machine learning pipelines for model training, deployment, monitoring, and lifecycle management.
• Design and implement frameworks for multi-agent AI systems, emphasizing state management, reliability, and long-running autonomous workflows.
• Architect and enhance Retrieval-Augmented Generation (RAG) pipelines and advanced context management strategies to improve model accuracy, relevance, and response quality.
• Develop platform-level tools for prompt engineering, automated evaluation, prompt optimization, and experimentation.
• Deploy, monitor, and maintain machine learning and generative AI models in production environments.
• Implement robust MLOps practices, including model versioning, observability, monitoring, and automated deployment pipelines.
• Collaborate with cross-functional teams to design, develop, and deliver AI-powered products and services.
• Optimize system performance, scalability, and reliability for high-volume production workloads.
• Stay current with emerging technologies, frameworks, and best practices in machine learning and generative AI.

Required Qualifications

• Bachelor's degree in Computer Science, Machine Learning, Artificial Intelligence, Software Engineering, or a related field (or equivalent practical experience).
• 5+ years of experience in machine learning engineering, software engineering, or related technical roles.
• Strong experience designing and developing distributed systems and scalable backend architectures.
• Deep understanding of the end-to-end machine learning lifecycle, including data ingestion, model training, evaluation, deployment, monitoring, and maintenance.
• Hands-on experience building applications using Large Language Models (LLMs), including Retrieval-Augmented Generation (RAG) architectures and advanced prompt engineering techniques.
• Experience deploying, scaling, and maintaining machine learning models in production environments.
• Strong programming skills in Python.
• Experience with modern machine learning frameworks such as PyTorch.
• Strong understanding of software engineering best practices, including testing, version control, and code quality.
• Excellent analytical, problem-solving, and communication skills.

Preferred Qualifications

• Experience with distributed task queues or workflow orchestration frameworks for managing complex, multi-stage AI processes.
• Experience with frameworks that support horizontal scaling of compute-intensive machine learning workloads.
• Knowledge of agentic AI architectures, including multi-agent systems, tool integration, self-correction, and iterative reasoning workflows.
• Familiarity with vector databases, embedding technologies, and high-throughput data processing pipelines.
• Experience implementing MLOps practices, CI/CD pipelines, and cloud-based machine learning infrastructure.
• Familiarity with cloud platforms such as AWS, Azure, or Google Cloud Platform.
• Experience with containerization and orchestration technologies such as Docker and Kubernetes.