• Build and own core platform capabilities, user-facing experiences and backend services— delivering end-to-end features from requirements to production. • Drive architecture and technical direction for integration-heavy systems, helping the team make durable decisions around scalability, reliability, maintainability, and operational safety. • Own reliability and production-grade behavior, including safe shutdown and edge-case handling, and proactively debug issues across distributed systems. • Build secure, compliant integrations and user experiences, including stronger validation, security hardening, and privacy-minded product changes. • Deliver user-facing UX improvements that reduce friction and increase clarity, ensuring internal and external tools are fast, intuitive, and accessible. • Collaborate cross-functionally and drive projects to completion, partnering with Product, QA, Design, and other engineering teams to deliver roadmap items and high-severity fixes. • Raise engineering standards through code review and technical mentorship, setting patterns for maintainability, observability, and robust integration design.

• Drive the on-site deployment, integration, and scaling of enterprise Generative AI solutions. • Embed directly within customer engineering teams to operationalize Large Language Models (LLMs) and retrieval systems across multi-cloud environments (AWS, Azure, GCP). • Bridge the gap between AI research and production-grade cloud infrastructure. • Collaborate with cross-functional teams and business partners to leverage analytical skills and ensure business value.

Role Description We are looking for an LLM Engineer to design, execute, and operationalize fine-tuning workflows for large language models across supervised, preference-based, and reinforcement learning approaches. The role requires deep practical experience with modern training stacks, careful dataset construction, rigorous evaluation methodology, and the engineering discipline to operate complex training pipelines reliably. The ideal candidate combines strong ML intuition with production-grade engineering practices, and is comfortable navigating the trade-offs between data quality, compute budget, evaluation rigor, and shipping velocity. In this role you will work closely with cross-functional partners — product, design, engineering, operations, and business stakeholders — to translate ambiguous requirements into well-engineered solutions, and will be expected to raise the bar through code review, design review, and mentorship of more junior engineers. The successful candidate brings strong engineering discipline, a clear communication style, and a track record of shipping meaningful work that holds up well in production. Key Responsibilities - Design and execute fine-tuning experiments for large language models using supervised, DPO, RLHF, and related techniques. - Lead dataset construction, curation, and quality assurance processes for instruction tuning and preference data. - Build scalable training pipelines on top of modern distributed training frameworks. - Tune hyperparameters, optimizer configurations, and training stability strategies for large-model fine-tuning. - Implement parameter-efficient fine-tuning techniques such as LoRA, QLoRA, and adapter-based methods. - Design rigorous evaluation suites including automated benchmarks, human evaluation, and capability-specific probes. - Implement safety, refusal, and policy evaluations to track model behavior across releases. - Operate large-scale training jobs on GPU clusters, diagnosing failures and recovering training state reliably. - Optimize training throughput using mixed precision, sequence packing, and efficient attention implementations. - Manage model artifacts, lineage tracking, and reproducibility across many concurrent experiments. - Collaborate with product, research, and platform teams to align fine-tuning roadmaps with business needs. - Document training methodology, results, and decisions clearly for technical and non-technical audiences. - Mentor engineers on fine-tuning best practices, evaluation rigor, and responsible deployment. - Stay current with LLM research and translate advances into production-ready fine-tuning recipes. Qualifications - Master’s or PhD in Computer Science, Machine Learning, or a related field; or equivalent experience. - Six or more years of combined ML research and engineering experience, with significant LLM exposure. - Strong proficiency in Python and modern deep learning frameworks, especially PyTorch. - Hands-on experience fine-tuning transformer-based language models at non-trivial scale. - Familiarity with distributed training strategies including FSDP, ZeRO, and pipeline parallelism. - Experience with RLHF, DPO, or other preference optimization techniques. - Strong understanding of evaluation methodology, benchmarks, and human evaluation design. - Experience operating training jobs on GPU clusters and recovering from failures. - Strong written and verbal communication skills. - Track record of shipping or publishing impactful LLM work. Preferred Qualifications - Publications at top-tier ML venues. - Experience with multimodal model fine-tuning. - Familiarity with synthetic data generation and dataset distillation. - Open-source contributions to LLM training libraries. - Exposure to responsible AI evaluation and red-teaming practices. How to Apply Would you like to know more about this opportunity? For immediate consideration, please send your resume to [email protected] or contact us at (908) 505-3899. Learn more about Bright Vision Technologies at www.bvteck.com .

• Collaborate with product managers, data scientists, and software engineers to identify AI/ML opportunities and develop innovative solutions. • Implement and deploy AI/ML algorithms and models into production environments. • Optimize and fine-tune AI/ML models to improve accuracy and efficiency. • Conduct experiments, perform data analysis, and present findings to stakeholders. • Develop and maintain documentation for AI/ML algorithms, models, and solutions. • Stay up-to-date with the latest AI/ML research and technologies and apply them to improve our platform. • Mentor and provide technical guidance to junior AI/ML engineers or team members.