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About This Role
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 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 tremendous career growth potential.
LLM Engineer
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Job Title: LLM 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 annnum
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 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.
Required 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\-3545\. 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.
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Role Details
About This Role
LLM Engineers specialize in building applications powered by large language models. They design RAG systems, fine-tune models, build agent frameworks, and optimize inference pipelines for cost and latency. This is the role that didn't exist three years ago and now has thousands of open positions.
The scope is broad. You might be building a customer support chatbot that needs to pull from a knowledge base of 50,000 documents, or designing an agent that can navigate a company's internal tools to complete multi-step tasks. The common thread is taking a foundation model and making it do something useful, reliably, at scale, without bankrupting the company on API costs.
Across the 3,708 AI roles we're tracking, LLM Engineer positions make up 0% of the market. At BV Teck, this role fits into their broader AI and engineering organization.
LLM Engineer is one of the fastest-growing AI job titles. Every company building AI-powered products needs people who understand the full stack: from embedding models to vector stores to inference optimization. The supply of experienced LLM engineers is thin because the field is so new, which keeps compensation high and demand strong.
What the Work Looks Like
A typical week includes: building and testing RAG pipelines (chunking strategies, embedding models, retrieval evaluation), debugging why the agent took a wrong action path, optimizing inference costs (caching, batching, model selection), and working with the product team on new LLM-powered features. You'll context-switch between deep technical work and cross-functional collaboration.
LLM Engineer is one of the fastest-growing AI job titles. Every company building AI-powered products needs people who understand the full stack: from embedding models to vector stores to inference optimization. The supply of experienced LLM engineers is thin because the field is so new, which keeps compensation high and demand strong.
Skills Required
RAG and vector databases are the most common requirements. Expect to work with LangChain or LlamaIndex, embedding models, and at least one vector store (Pinecone, Weaviate, Chroma). Python is non-negotiable. Understanding the cost/latency/quality tradeoffs between different model providers and architectures is what separates senior from junior engineers.
Fine-tuning experience is valuable for specific use cases but most production LLM work is RAG-based. Agent frameworks (LangGraph, CrewAI, custom orchestration) are increasingly important as companies move beyond simple chat interfaces. Evaluation and observability tools (LangSmith, Arize, custom dashboards) are essential for production deployments.
Look for roles that specify the production stack, mention specific use cases, and talk about cost optimization. Companies that understand LLM engineering will mention evaluation methodology, latency requirements, and scale targets. Vague 'build AI features' postings often mean they haven't figured out their architecture yet.
Compensation Benchmarks
LLM Engineer roles pay a median of $203,940 based on 9 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($125K) sits 39% below the category median. Disclosed range: $100K to $150K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
BV Teck AI Hiring
BV Teck has 40 open AI roles right now. They're hiring across AI/ML Engineer, MLOps Engineer, LLM Engineer, Research Engineer. Positions span Tempe, AZ, US, Hoboken, NJ, US, Remote, US. Compensation range: $150K - $175K.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% of all AI roles offer remote work.
Career Path
Common paths into LLM Engineer roles include Software Engineer, ML Engineer, Data Engineer.
From here, career progression typically leads toward AI Architect, Principal Engineer, AI Engineering Manager.
The fastest path is through software engineering. If you can build production systems and you understand LLM capabilities and limitations, you're already qualified for most roles. Build a portfolio project that demonstrates RAG implementation, evaluation, and cost optimization. Open-source contributions to LLM frameworks are strong signals to hiring managers.
What to Expect in Interviews
Technical screens cover RAG architecture design, embedding model selection, chunking strategies, and retrieval evaluation. Expect questions about cost optimization: how you'd reduce inference costs by 50% without degrading quality. System design rounds often present scenarios like 'design a customer support chatbot that can access 100K documents' and evaluate your understanding of the full stack from embedding to serving.
When evaluating opportunities: Look for roles that specify the production stack, mention specific use cases, and talk about cost optimization. Companies that understand LLM engineering will mention evaluation methodology, latency requirements, and scale targets. Vague 'build AI features' postings often mean they haven't figured out their architecture yet.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
LLM Engineer is one of the fastest-growing AI job titles. Every company building AI-powered products needs people who understand the full stack: from embedding models to vector stores to inference optimization. The supply of experienced LLM engineers is thin because the field is so new, which keeps compensation high and demand strong.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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