Senior Machine Learning Engineer; Inference Platform

At Wizard AI, we’re building the top-performing AI Shopping Agent that delivers the best products from across the web with unmatched accuracy, quality, and trust. Our ML models power the core of our platform, and we’re seeking an experienced Senior MLOps Engineer to take ownership of how our machine learning systems run reliably and efficiently in production.

As a Senior MLOps Engineer at Wizard, you’ll own the end-to-end ML lifecycle – from model packaging and deployment to monitoring, observability, optimization and scaling – for a custom-built inference platform powering a live conversational shopping agent. This is not a standard cloud ML pipeline role; we run multiple specialized inference engines handling real-time inference for high-stakes shopping decisions, and the work requires both hands-on operational depth and the architectural judgement to evolve the platform as Wizard scales.

You’ll work closely with ML Engineers, Data teams, and Dev Ops, with real influence over how the infrastructure is designed – not just how it runs.

• Build, maintain, and optimize production-grade ML pipelines, enabling seamless transitions from experimentation to production.
• Define and implement strategies for model versioning, rollout, rollback, and lifecycle management to ensure robust and reproducible ML systems.
• Define and enforce serving-layer SLAs – latency, availability, GPU utilization, TTFT, ITL – and build observability and alerting.
• Apply software engineering best practices including testing, CI/CD integration, and reproducibility to ML workflows, improving iteration speed for ML engineers without compromising reliability.
• Ensure ML systems are secure, cost-efficient, and scalable, partnering with Dev Ops on infrastructure standards while owning ML-specific operational concerns.
• Collaborate cross-functionally with ML, Data, Product, and Dev Ops teams to translate ML requirements into production-ready systems and influence technical planning and roadmap decisions.

• Bachelor’s or Master’s degree in Computer Science, Data Science, or a related field, or equivalent experience.
• 5-8+ years of experience in Software Engineering, ML Engineering, Platform Engineering, or Infrastructure Engineering with direct ownership of production ML serving systems.
• Hands-on experience deploying and maintaining LLMs and deep learning models, in production environments.
• Strong Python skills and software engineering fundamentals with infrastructure depth. Familiarity with ML frameworks (PyTorch, Tensorflow or similar) is preferred.
• Experience with cloud platforms such as AWS, GCP, or Azure, and familiarity with ML lifecycle tooling, including model registries and experimentation platforms.
• Familiarity with inference optimization at the hardware and systems level – batching strategies, memory management, quantization tradeoffs, CPU/GPU interaction patterns.
• Demonstrated ability to reason about tradeoffs between latency, cost, throughput, and reliability at the systems as well as operational level.
• Experience in high-growth startup environments and an ability to thrive in a fast-paced, evolving technical landscape.

• Reliable, Scalable ML Systems: Production models run with clear SLAs, minimal downtime, and full observability – latency, availability, and GPU utilization tracked and enforced. Deployment pipelines handle growth and evolving AI requirements.
• End-to-End Ownership: You own the full ML lifecycle – from packaging and deployment through monitoring and optimization – enabling ML engineers to iterate quickly while maintaining reproducibility, reliability and security.
• Influence and Impact: You shape the technical roadmap for ML operations, collaborating with ML, Data, and Dev Ops teams to improve system performance, reduce operational costs, and drive the overall AI strategy forward.

The expected base salary range for this role is $200,000 – $250,000 USD, and will vary based on skills, experience, role level, and geographic location. Final compensation will be determined by considering these factors alongside overall role scope and responsibilities.

In addition to…