MLOps Engineer

Role Description

We are seeking a MLOps Engineer to design, build, and operate high-performance, highly reliable inference platforms for serving large machine learning models in production. The role focuses on the systems engineering side of AI deployment, including:

• Request routing
• Batching
• Caching
• Autoscaling
• GPU utilization
• End-to-end observability across diverse model workloads

The ideal candidate brings strong distributed systems and performance engineering expertise, has shipped serving systems at scale, and understands the trade-offs between latency, throughput, cost, and quality in ML serving.

Qualifications

• Bachelor’s or Master’s degree in Computer Science or a related field
• Six or more years of experience in distributed systems, infrastructure, or ML platform engineering
• Strong proficiency in Python and a systems language such as Go, Rust, or C++
• Deep experience operating high-throughput, low-latency services in production
• Hands-on experience with LLM or large model inference frameworks such as vLLM or TensorRT-LLM
• Strong understanding of GPU architecture, memory hierarchies, and accelerator utilization
• Familiarity with Kubernetes, autoscaling, and modern cloud platforms
• Experience with observability stacks including metrics, tracing, and structured logging
• Solid grounding in performance engineering and capacity planning
• Strong communication and incident response skills

Requirements

• Design and operate model serving platforms supporting diverse workloads including LLMs, vision models, and recommendation systems
• Optimize inference performance using continuous batching, paged attention, speculative decoding, and request multiplexing
• Implement multi-tenant routing, rate limiting, and quality-of-service policies across model endpoints
• Build autoscaling and capacity management systems that balance latency, throughput, and cost
• Tune GPU utilization, memory management, and KV cache strategies for LLM serving workloads
• Integrate model serving with API gateways, identity systems, and observability platforms
• Implement caching, prompt deduplication, and response reuse strategies where appropriate
• Drive end-to-end observability including latency histograms, queue dynamics, GPU utilization, and error tracking
• Develop deployment workflows including canary releases, shadow testing, and automated rollback
• Operate incident response for high-availability AI services and drive durable reliability improvements
• Collaborate with ML and product teams to support new model releases and capability rollouts
• Implement security controls including request signing, content filtering, and abuse detection at the serving layer
• Document operational procedures, performance characteristics, and tuning guidance for internal teams
• Stay current with AI serving research and translate advances into production capabilities

Benefits

• 100% Remote work
• Full-time, Direct W2 position
• Salary Range: $100,000–$150,000 Annually
• Career growth potential