ML Platform Engineer

Role Description

We are seeking a ML Platform 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

• Competitive base salary commensurate with experience, plus benefits.
• Long-term, multi-year engagement aligned to the Bright Vision SOW delivery roadmap.

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 .