Model Serving Engineer at Bright Vision Technologies

Edison, New Jersey, United States -

Full Time

Start Date

Immediate

Expiry Date

15 Aug, 26

Salary

0.0

Posted On

17 May, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Model Serving, Distributed Systems, Python, Go, Rust, C++, vLLM, TensorRT-LLM, GPU Architecture, Kubernetes, Performance Engineering, Observability, Capacity Planning, Inference Optimization, API Gateways, ML Platform Engineering

Industry

Information Technology & Services

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 Model Serving 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. Model Serving Engineer Job Title: Model Serving 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 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. 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/1099/3RD PARTY COMPANIES. ALL OUR ROLES ARE W2 AND NO 3RD PARTY BROKERING PLEASE. 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 Model Serving 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 vcLLM 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. How to Apply Would you like to know more about this opportunity? For immediate consideration, please send your resume to venkat.r@bvteck.com or contact us at (908) 505-3899. Learn more about Bright Vision Technologies at www.bvteck.com. We recognize that our people are our strength, and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants’ and employees’ religious practices and beliefs, as well as mental health or physical disability needs. Bright Vision Technologies is an Equal Opportunity Employer, including Disability/Veterans. Position offered by “No Fee Agency.” 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.

Responsibilities

Design, build, and operate high-performance inference platforms for serving large machine learning models in production. Focus on systems engineering tasks including request routing, GPU utilization, and end-to-end observability for diverse AI workloads.