AI/ML Engineer Needed for Clustering Model
Budget: $25 – $50 USD
I'm in need of a skilled AI/ML engineer based in China
Responsibilities —
• Execution of large-scale AI/ML projects, ensuring alignment with business objectives.
• Architect and design scalable, low-latency backend architectures, focusing on AI/ML model serving, real-time data processing, and integration with cloud infrastructure.
• Oversee deployment of AI/ML models and infrastructure, ensuring high-performance, reliability, and scalability in production environments.
• Drive decisions for cloud platforms (AWS, GCP, or Azure), auto-scaling, serverless architectures, and resource optimization.
• Influence and guide the architectural direction of the organization, ensuring future-proof, AI/ML-ready systems and solutions.
• Define benchmarks and optimize performance across AI/ML workloads.
• Lead technical troubleshooting, performance tuning, and architecture optimizations to ensure scalable and efficient deployment of AI/ML systems.
• Mentor and provide technical guidance to engineers across multiple teams, fostering a collaborative and innovative engineering culture.
You need —
• 4+ years in software engineering or architecture, including 5+ years leading cross-functional teams in AI/ML or distributed systems projects.
• Proven experience designing and managing large-scale backend architectures and distributed environments, with expertise in microservices, event-driven systems, and RESTful/GraphQL API development for AI/ML systems in production.
• Extensive experience with AI/ML model deployment, performance tuning, model serving, and seamless integration with backend systems.
• Deep understanding of cloud platforms (AWS, GCP, Azure), auto-scaling, serverless, and fault-tolerant architectures.
• Strong experience in machine learning frameworks (TensorFlow, PyTorch) and data pipelines for real-time processing and high-concurrency environments.
• Ability to troubleshoot and resolve complex technical challenges related to AI/ML workloads, scalability, and performance.
• Excellent leadership and communication skills, with a proven ability to mentor engineers and work closely with cross-functional stakeholders.
• Self-starter who excels at execution, balancing short-term technical delivery with long-term scalability and efficiency goals.
Bonus points for —
• Experience with AI/ML infrastructure tools like MLFlow, Kubeflow, or MLOps best practices.
• Experience with real-time data processing & streaming technologies (Apache Kafka, Flink, Spark).
• Familiarity with NLP frameworks (e.g., Hugging Face, spaCy)
• Open-source contributions, side projects, or thought leadership through technical content (blogs, videos).
• Knowledge of data privacy and security regulations relevant to AI/ML deployments.
Responsibilities —
• Execution of large-scale AI/ML projects, ensuring alignment with business objectives.
• Architect and design scalable, low-latency backend architectures, focusing on AI/ML model serving, real-time data processing, and integration with cloud infrastructure.
• Oversee deployment of AI/ML models and infrastructure, ensuring high-performance, reliability, and scalability in production environments.
• Drive decisions for cloud platforms (AWS, GCP, or Azure), auto-scaling, serverless architectures, and resource optimization.
• Influence and guide the architectural direction of the organization, ensuring future-proof, AI/ML-ready systems and solutions.
• Define benchmarks and optimize performance across AI/ML workloads.
• Lead technical troubleshooting, performance tuning, and architecture optimizations to ensure scalable and efficient deployment of AI/ML systems.
• Mentor and provide technical guidance to engineers across multiple teams, fostering a collaborative and innovative engineering culture.
You need —
• 4+ years in software engineering or architecture, including 5+ years leading cross-functional teams in AI/ML or distributed systems projects.
• Proven experience designing and managing large-scale backend architectures and distributed environments, with expertise in microservices, event-driven systems, and RESTful/GraphQL API development for AI/ML systems in production.
• Extensive experience with AI/ML model deployment, performance tuning, model serving, and seamless integration with backend systems.
• Deep understanding of cloud platforms (AWS, GCP, Azure), auto-scaling, serverless, and fault-tolerant architectures.
• Strong experience in machine learning frameworks (TensorFlow, PyTorch) and data pipelines for real-time processing and high-concurrency environments.
• Ability to troubleshoot and resolve complex technical challenges related to AI/ML workloads, scalability, and performance.
• Excellent leadership and communication skills, with a proven ability to mentor engineers and work closely with cross-functional stakeholders.
• Self-starter who excels at execution, balancing short-term technical delivery with long-term scalability and efficiency goals.
Bonus points for —
• Experience with AI/ML infrastructure tools like MLFlow, Kubeflow, or MLOps best practices.
• Experience with real-time data processing & streaming technologies (Apache Kafka, Flink, Spark).
• Familiarity with NLP frameworks (e.g., Hugging Face, spaCy)
• Open-source contributions, side projects, or thought leadership through technical content (blogs, videos).
• Knowledge of data privacy and security regulations relevant to AI/ML deployments.