Senior ML Ops Engineer

Wizard

4d•$200,000 - $250,000•Remote

About The Position

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. The Role 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 DevOps, with real influence over how the infrastructure is designed – not just how it runs.

Requirements

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.

Responsibilities

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 DevOps on infrastructure standards while owning ML-specific operational concerns.
Collaborate cross-functionally with ML, Data, Product, and DevOps teams to translate ML requirements into production-ready systems and influence technical planning and roadmap decisions.