Senior AI/ ML Engineer (W2 Only)

Job Title: Senior Machine Learning Engineer / ML Systems Architect

Location: San Jose CA - Onsite

Experience: 6–12 Years

Role Overview

We are looking for a hands-on Machine Learning Engineer with strong experience in designing and implementing scalable ML systems. The ideal candidate should have recent project experience (within the last 12 months) and proven ownership across the ML and MLOps lifecycle.

This role requires strong system thinking, optimization expertise, and the ability to work cross-functionally with engineering and product teams.

Key Responsibilities

• Design and implement end-to-end scalable ML systems
• Own full or partial ML & MLOps lifecycle, including:
• Evaluation techniques
• Machine learning algorithms
• Statistical modeling
• Model training & optimization
• End-to-end deployment
• Metric generation & monitoring
• Perform ML model optimization (quantization, pruning, speculative decoding, etc.)
• Identify system bottlenecks and implement performance improvements
• Build and manage CI/CD pipelines for ML workflows
• Collaborate with cross-functional teams and lead inter-team technical discussions
• Ensure clear, concise technical documentation
• Deliver projects within defined timelines

Required Technical Skills

• Hands-on experience designing and deploying scalable ML systems (recent projects within last 12 months)
• Strong expertise in:
• Machine Learning Algorithms
• Statistical Modeling
• Model Monitoring & Deployment
• Prompt Engineering
• Programming proficiency in:
• Java (library/dependency management, packaging, algorithms)
• Python
• TensorFlow / PyTorch
• Strong understanding of:
• DevOps principles (CI/CD pipelines, Terraform)
• Containerization technologies (Docker, Kubernetes)
• Experience in ML system-level performance optimization

Good to Have

• Experience with Google Cloud Platform ML tech stack
• Infrastructure as Code (IaC)
• Big data technologies (Apache Spark, Hadoop)
• Knowledge of ethical ML practices (bias mitigation, model fairness)
• Strong data analytics capability (anomaly, skew detection)
• Experience building on-device ML systems
• Mentoring and leadership exposure
• Project management experience