AI Platform Engineer

AI Platform Engineer

Bright Vision Technologies is a forward-thinking software development company dedicated to building innovative solutions that help businesses automate and optimize their operations. We leverage cutting-edge technologies to create scalable, secure, and user-friendly applications. As we continue to grow, we're looking for a skilled AI Platform Engineer to join our dynamic team and contribute to our mission of transforming business processes through technology. This is a fantastic opportunity to join an established and well-respected organization offering tremendous career growth potential.

Job Title: AI Platform Engineer Location: 100% Remote (Continental United States) Position Type: In-house Bright Vision Technologies SOW engagement (no third-party client or vendor) Experience: 6+ years Salary Range: $100k to $150k per annum Sponsorship: No new H1B sponsorship available. H1B transfers welcomed for qualified candidates. Employment Type: Full-time, direct W2 with Bright Vision Technologies (no C2C, no 1099, no third-party) Engagement: Long-term, multi-year, aligned to the Bright Vision SOW delivery roadmap Compensation: Competitive base salary commensurate with experience, plus benefits.

Job Summary We are seeking a AI 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, and end-to-end observability across diverse model workloads.

• 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.

Required 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.

Preferred Qualifications

• Open-source contributions to model serving infrastructure.
• Experience with multi-region or globally distributed AI serving.
• Familiarity with model quantization, distillation, and compression techniques.
• Exposure to FinOps for AI workloads and cost-efficient serving design.
• Experience supporting external-facing AI APIs at scale.