Senior ML Engineer

Summary: We are seeking a Senior ML Engineer with extensive experience in machine learning inference, production deployment, and system design, particularly within cloud-native ML platforms. This hands-on role focuses on deploying, scaling, benchmarking, and monitoring machine learning models in production environments. The ideal candidate will have a strong background in software engineering and machine learning engineering, with a focus on optimizing model-serving architectures. Collaboration with software engineering and ML research teams is essential for operationalizing models at scale.

Key Responsibilities:

• Design, deploy, and optimize machine learning inference solutions in production environments.
• Evaluate inference frameworks and implement scalable model-serving architectures.
• Automate deployment workflows and operational processes on Google Cloud Platform (Google Cloud Platform).
• Design and execute performance benchmarking, quality testing, and validation frameworks for machine learning models.
• Monitor production model behavior and drive continuous improvements in performance, reliability, and scalability.
• Architect solutions across hybrid environments spanning on-premise infrastructure and Google Cloud Platform.
• Integrate machine learning models into enterprise applications and real-time streaming platforms.
• Collaborate with software engineering and ML research teams to operationalize models at scale.
• Participate in architecture reviews and contribute to distributed systems design decisions.
• Support production troubleshooting, performance tuning, and platform optimization.

Key Skills:

• 8+ years of software engineering, machine learning engineering, or platform engineering experience.
• Strong experience deploying and managing ML inference workloads on Google Cloud Platform (Google Cloud Platform).
• Hands-on experience with model serving, inference optimization, and deployment automation.
• Strong system design skills with experience designing scalable distributed systems.
• Experience operating within hybrid architectures combining cloud and on-premise infrastructure.
• Experience integrating ML models into production applications and streaming pipelines.
• Exposure to Java-based systems, event-driven architectures, or real-time data processing platforms.
• Strong understanding of benchmarking, performance testing, quality validation, and production monitoring.
• Working knowledge of machine learning frameworks including TensorFlow, PyTorch, and JAX.
• Strong troubleshooting, debugging, and operational support skills.

Salary (Rate): undetermined

City: undetermined

Country: undetermined

Working Arrangements: remote

IR35 Status: undetermined

Seniority Level: undetermined

Industry: IT