MLOps Engineer

Bright Vision Technologies; Sugar Land (TX); Remote; USD 100,000 - 150,000; Full time

Job summary

Bright Vision Technologies is seeking an experienced MLOps Engineer to design, build, and operate high-performance inference platforms for serving large ML models in production. The role concentrates on systems engineering for AI deployment—routing, batching, caching, autoscaling, GPU utilization, and end-to-end observability across diverse workloads.

The ideal candidate has strong distributed systems and performance engineering expertise, successfully shipped serving systems at scale, and

Qualifications

• Bachelor’s or Master’s degree in Computer Science or a related field.
• 6+ years of experience in distributed systems or ML platforms.
• Proficient in Python and at least one systems language (Go, Rust, or C++).
• Experience with LLM inference frameworks such as vLLM or TensorRT-LLM.
• Strong knowledge of GPU architectures and memory hierarchies.

Responsibilities

• Design and operate model serving platforms supporting diverse workloads (LLMs, vision, recommendations).
• Optimize inference performance with batching, speculative decoding, and request multiplexing.
• Implement multi-tenant routing and QoS across model endpoints.
• Build autoscaling and capacity management balancing latency, throughput, and cost.
• Collaborate with ML and product teams to support model releases and capabilities.

Skills

Distributed systems

Performance engineering

Observability

Incident response

Communication

Education

Bachelor's or Master's in Computer Science

Tools

Python

Go

Rust

C++

Kubernetes

TensorRT-LLM

Job description

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 MLOps 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: MLOps Engineer Location: 100% Remote (Continental United States) Position Type: In-house Bright Vision Technologies SOW engagement (no third-party client or vendor) Salary : $100K - $150K Experience: 6+ years

Employment Terms & Visa Policy

This is a 100% remote, full-time, direct W2 position with Bright Vision Technologies. This role is part of Bright Vision Technologies' in-house Statement of Work (SOW) engagement. The client, end customer, and employer for this position is Bright Vision Technologies — there is no third-party client, vendor, or implementation partner involved. We do not engage in C2C, 1099, or third-party arrangements for this role. BUT STRICTLY NO C2C/1099. All our roles are W2. Candidates must be willing to work directly as a full-time W2 employee of Bright Vision Technologies and contribute to our in-house SOW deliverables. No new H1B sponsorship is available for this role. However, candidates who are currently on a valid H1B visa and require a transfer are welcome to apply. We will support H1B transfers for qualified candidates. For every role, a technical coding assessment is mandatory. Please apply only if you are confident in your technical abilities and hands-on experience.

Job Summary

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, and 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.

Key Responsibilities

• 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.

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. Any improper interference with employees' ability to perform their job duties may result in disciplinary action up to and including termination of employment.