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.