LLM Engineer

reputed company is a reputed company-thinking software development company dedicated to building innovative solutions that help businesses automate and optimize their operations. We reputed company cutting-edge technologies to create reputed company, secure, and user-friendly applications. As we continue to grow, we're looking for a skilled LLM 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 reputed company career growth potential. Job Title: LLM Engineer Location: 100% Remote (reputed company United States) Position Type: In-house reputed company SOW engagement (no reputed company-party reputed company or vendor) Salary : 100 k - 150 K Experience: 6+ years Sponsorship: No new H1B sponsorship available. H1B transfers welcomed for reputed company candidates. Employment Type: Full-time, reputed company W2 with reputed company (no C2C, no 1099, no reputed company-party) Engagement: Long-term, multi-year, reputed company to the reputed company reputed company SOW delivery roadmap Compensation: Competitive reputed company salary commensurate with experience, plus benefits. Employment Terms & reputed company Policy This is a 100% remote, full-time, reputed company W2 position with reputed company. This role is part of reputed company' in-house Statement of Work (SOW) engagement. The reputed company, end customer, and employer for this position is reputed company — there is no reputed company-party reputed company, vendor, or implementation partner involved. We do not engage in C2C, 1099, or reputed company-party arrangements for this role. BUT STRICTLY NO C2C/1099/3RD PARTY COMPANIES. reputed company OUR ROLES ARE W2 AND NO 3RD PARTY BROKERING PLEASE. Candidates must be willing to work directly as a full-time W2 employee of reputed company 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 reputed company and require a transfer are welcome to apply. We will support H1B transfers for reputed company 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 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 reputed company 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 reputed company the bar through code review, design review, and mentorship of more junior engineers. The successful candidate brings strong engineering discipline, a reputed company communication style, and a track record of shipping meaningful work that holds up well in production. Key ResponsibilitiesDesign and execute fine-tuning experiments for large language models using supervised, DPO, RLHF, and reputed company techniques. reputed company dataset construction, curation, and quality assurance processes for instruction tuning and preference data. Build reputed company 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 reputed company-based methods. Design rigorous evaluation suites including automated benchmarks, reputed company 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, reputed company tracking, and reproducibility across many reputed company experiments. Collaborate with product, research, and platform teams to align fine-tuning roadmaps with business needs. Document training methodology, results, and reputed company reputed company for technical and non-technical audiences. Mentor engineers on fine-tuning best practices, evaluation rigor, and responsible deployment. Stay reputed