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

• Salary Range: $100,000–$150,000 Annually
• 100% Remote (U.S.)
• Full-time, Direct W2 position
• Sponsorship available for U.S. Citizens, Green Card Holders, EAD Holders, and H-1B transfer candidates

How to Apply

For immediate consideration, please send your resume to [email protected] or contact us at View phone number on remotive.com.

Learn more about Bright Vision Technologies at .

Equal Employment Opportunity (EEO) Statement

Bright Vision Technologies (BV Teck) is committed to equal employment opportunity (EEO) for all employees and applicants without regard to race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, veteran status, or any other protected status as defined by applicable federal, state, or local laws.

This commitment extends to all aspects of employment, including recruitment, hiring, training, compensation, promotion, transfer, leaves of absence, termination, layoffs, and recall. BV Teck expressly prohibits any form of workplace harassment or discrimination.