AI Engineer to Optimize Existing LLM Performance and more

@koolvishruth

As an AI engineer with a strong background in large language models (LLMs) and natural language processing, I am confident that I can provide the expertise you need to optimize your existing LLM's performance. My 8+ years of experience in software development and machine learning, coupled with my deep understanding of Python and AI, makes me uniquely equipped for this project.

@katerynai2

Hello, As an AI development specialist with a solid background in full-stack engineering, I am highly experienced in understanding and enhancing existing codebases for maximum performance. I have a sharp eye when it comes to diagnosing and resolving bottlenecks - skills that align perfectly with the objectives of your project. Given access to your checkpoints, tokenizer and evaluation harness, I will leave no stone unturned in optimizing your large-language model. Whether it entails fine-tuning, pruning or knowledge-distillation, my goal is to improve accuracy and reduce response time without compromising stability. Though my past projects have primarily centered on Ethereum, L2 rollups and React plus Node stacks, my greatest asset is adaptability. I believe this flexibility makes me uniquely suited to explore and apply innovative techniques beyond what you've proposed. Should our collaboration prove productive, I am more than open to expanding my capabilities in line with your needs - turning this potential professional relationship into a rich landscape of ongoing iteration and evolution for your AI system. Let's take this pilot project and transform it into something truly transformative together! Thanks!

@matin1106

Dear Syed Sajid I., I saw your project titled "AI Engineer to Optimize Existing LLM Performance and more" and I'm interested in submitting a proposal. With over 10 years of experience in software development, I have a proven track record and strong expertise in the required skillset, including Large Language Models (LLMs). Here's a bit more about my skillset: Skills: Natural Language Processing, AI Model Development, Large Language Models (LLMs), AI Development I'm confident I can deliver exceptional results for your project. Would you be open to discussing this further? Thank you for your time. Sincerely, A Mateen

@mugencodes

Hello Syed, I'm Jayabrata Bhaduri, an AI Engineer with over 10 years of experience in AI Model Development. I specialize in optimizing AI models for enhanced performance and accuracy. I understand your requirement to optimize your existing large-language model for improved accuracy and reduced response time. I will thoroughly analyze the current codebase and training pipeline to identify and address accuracy bottlenecks. By implementing techniques such as domain-specific fine-tuning, model pruning, and optimized batching, I will ensure that your model delivers more precise answers efficiently. Let's discuss further in the chat to explore how we can collaborate to achieve your project goals. Regards, Jayabrata Bhaduri

@adily1

Hey , I just finished reading the job description and I see you are looking for someone experienced in Natural Language Processing, AI Development, Large Language Models (LLMs) and AI Model Development. This is something I can do. Please review my profile to confirm that I have great experience working with these tech stacks. While I have few questions: 1. These are all the requirements? If not, Please share more detailed requirements. 2. Do you currently have anything done for the job or it has to be done from scratch? 3. What is the timeline to get this done? Why Choose Me? 1. I have done more than 250 major projects. 2. I have not received a single bad feedback since the last 5-6 years. 3. You will find 5 star feedback on the last 100+ major projects which shows my clients are happy with my work. Timings: 9am - 9pm Eastern Time (I work as a full time freelancer) I will share with you my recent work in the private chat due to privacy concerns! Please start the chat to discuss it further. Regards, Syed.

@uzairbz

Where are you seeing the bigger ceiling right now, hallucinations caused by weak domain alignment, or latency coming from the serving stack rather than the model itself? That distinction determines whether we optimize weights, architecture, or inference. I’d approach this as a targeted LLM performance audit, not blind fine-tuning. The first step is profiling: accuracy errors by category, token-level latency, batching behavior, and memory pressure during inference. How I’d tackle it Accuracy: error analysis on your eval set, domain-specific fine-tuning or adapter layers, retrieval grounding if needed, and controlled hallucination reduction. Speed: inference profiling, quantization or pruning where safe, optimized batching, and serving-stack improvements (token streaming, caching, GPU utilization). Stability: all changes validated against your BLEU/F1 benchmarks and real prompts before promotion. This works well as a pilot: we pick 1–2 measurable wins (accuracy ↑, latency ↓), ship them cleanly, and decide next steps based on results. Relevant background: I’ve optimized production LLM pipelines for both response quality and inference speed, focusing on practical gains rather than research-only tweaks. Once I review your checkpoints and harness, I’ll propose a concrete plan with tradeoffs and expected gains.

@rajeshrolen

Hello, Are you looking to take your existing large-language model to the next level of precision and efficiency? I understand the importance of optimizing your AI model for enhanced accuracy and reduced response time to meet the demands of your expanding scale. I have a proven track record in diving into codebases, diagnosing bottlenecks, and implementing solutions that result in improved performance. By leveraging techniques such as fine-tuning, model pruning, and optimized batching, I can help elevate your model's accuracy and speed without compromising stability. With access to your current resources and a collaborative approach, I am confident in my ability to deliver results that exceed your expectations. My technical skills, clear communication, and commitment to quality ensure a seamless development process and reliable post-launch support. You can explore my portfolio here: https://www.freelancer.com/u/rajeshrolen Let's discuss how we can optimize your existing LLM and achieve your project goals. Please feel free to open a chat with me to further explore this opportunity. Sincerely, Rajesh Rolen

@asadamyn

I've been there. Trained models that bench beautifully then choke in production. Chased hallucinations at 2am. Learned that accuracy and speed aren't tradeoffs—you're just looking at the wrong bottleneck. What I'll actually do: First, I'll tear through your pipeline like a code review from hell. Not to judge to find where compute's bleeding. tokenizer inefficiencies? Redundant forward passes? Batch sizes that made sense at 1k users but not 100k? I'll map it. Then the model. I'll run your eval harness against current checkpoints, segment where it fails (context length? edge cases? specific domains?), and test fixes: LoRA fine-tuning on your labeled corpus if it's knowledge gaps, pruning if it's bloat, distillation if you need leaner inference. I'll benchmark each—no black magic, just measured deltas on your metrics. For latency, quantization's obvious but risky. I'll profile serving stack first. Sometimes it's not the model, it's how you're batching requests or moving tensors. I've cut 40ms to 8ms without touching weights. Deliverable: Working checkpoint + optimized inference path, documented. Plus a brutally honest report: what worked, what didn't, what I'd try next. Why me: I don't do portfolio theater. I do 'show me your worst inference example and I'll tell you why it broke.' That's the relevant experience. Send worst failure case + current latency numbers. I'll tell you if I'm the right fix before you pay anything.

@ajinkyag77

Hello, I’d be interested in working on this pilot project to improve both the accuracy and latency of your existing LLM. My approach would begin with a focused audit of the current training pipeline, evaluation metrics, and inference setup to identify the main sources of hallucination and performance bottlenecks. Based on findings, I can propose and apply targeted techniques such as domain-specific fine-tuning, prompt and tokenizer adjustments, pruning or quantization for faster inference, and serving-level optimizations (batching, caching, or model loading strategies). Accuracy improvements would be validated against your internal BLEU/F1 benchmarks, while latency gains would be measured end-to-end. I’m comfortable working directly with existing checkpoints, evaluation harnesses, and labeled text corpora, and treating this engagement as a practical pilot to demonstrate measurable improvements before any longer-term collaboration.

@Shrutibimpl

Hello, I will serve as your AI Engineer to diagnose and resolve the accuracy and latency bottlenecks in your production LLM. I will dive into your existing codebase, tokenizer, and evaluation harness. My approach will be to apply advanced optimization techniques such as additional domain-specific fine-tuning on your labeled text corpus, model quantization, and optimized batching to simultaneously enhance accuracy (higher BLEU/F1, fewer hallucinations) and drastically reduce end-to-end response time. I will also evaluate your current serving stack to recommend and implement refactoring that maximizes inference speed without compromising system stability. 1) Which specific Large Language Model (LLM) architecture (e.g., Llama, GPT, T5) is your current model based on? 2) Which deep learning framework (e.g., PyTorch, TensorFlow) is the current model trained in? 3) What is the current median end-to-end latency (in milliseconds) that you are trying to beat? Thanks, Bharat

@TheAIGuyOrg

Hi, You already have what most teams don’t: a live LLM, real users, and internal evals. That’s exactly where I’m most useful taking a “good enough” model and making it feel like a different product through accuracy and latency engineering. Here’s how I’d handle the pilot: 1. Deep dive: Inspect checkpoints, tokenizer, training code, eval harness, and serving stack to pinpoint where quality (BLEU/F1) and end‑to‑end latency are actually getting stuck. 2. Accuracy: Domain‑specific fine‑tuning, smarter sampling/decoding, and data curation to reduce hallucinations while preserving the model’s current tone and behavior. 3. Speed: Quantization and pruning where safe, distillation for high‑traffic paths, plus KV‑caching, better batching, and a modern engine (e.g., vLLM / TensorRT‑LLM) to bring p95/p99 down without destabilizing outputs. Instead of vague promises, I’d propose concrete pilot targets with you upfront (e.g., +X BLEU/F1 on your internal set and −Y% median & p95 latency under a realistic load), and then ship against those. My focus is applied LLM engineering: training pipelines, eval, and high‑throughput inference for real users, not just benchmarks. If this matches what you’re looking for, I’m ready to dive into the codebase, propose a roadmap in week one, and treat this as the start of a longer collaboration if I earn it.

@snehbharat

Hi there, I understand you need to optimize your LLM's accuracy and reduce response time. Enhancing performance is the key priority. Here's my approach: * Fine-tuning with domain-specific data. * Model pruning & quantization for speed. * Optimized batching and serving stack review. * Evaluation report/performance improvements, * First draft/prototype within 7 days * All source files included * Unlimited revisions Why choose me: I have specific expertise in LLMs, RAG, and fine-tuning. Expertise in Hugging Face and LangChain. Quick questions: 1. What is the current serving infrastructure? 2. What are the current evaluation metrics? I can start immediately. Let's discuss the details. Best regards, Team Mactix - AI, ML, LLM

@susmit108

I have prior industrial work experience dealing with faster agentic LLM system inference and reduced hallucination. From architecture point of view, we can distill a smaller model from the current LLM and finetune it to specific use case. This will help both in reducing hallucination and reduced inference latency.

@Dmitrydev1

Hi there, I’ve reviewed your production LLM setup and your goals to push accuracy while reducing end-to-end latency. I’ll start with a focused diagnostic pass through the current codebase, training pipeline, tokenizer, and evaluation harness to pinpoint accuracy bottlenecks and latency hot spots. From there, I’ll propose and implement a targeted mix of techniques: domain-focused fine-tuning (if helpful for your data), quantization and pruning for inference efficiency, possible knowledge distillation, optimized batching, and, if needed, a refreshed serving stack, with stable, observable gains in precision (lower hallucinations, higher BLEU/F1) and faster responses. I’ll work with your checkpoints, tokenizer, evaluation harness, and labeled corpus to produce measurable improvements and clear, reproducible results. The plan is modular so we can validate each change against your internal metrics before moving forward. Proposed cadence: initial diagnostic and quick wins within 1-2 weeks, full optimization iterations over the following 1-2 weeks, with a pilot deployment available for review. Deliverables will include a detailed evaluation report, modified model artifacts, and updated serving/configuration for orderly rollout. I’m ready to start quickly and align on constraints during a kickoff. Best regards, Dmytro

@chiragm078

I'm Chirag, an AI engineer with a strong background in Natural Language Processing and AI Development. I have immense experience in optimizing and fine-tuning existing models to maximize their performance. My expertise extends into implementing techniques such as model pruning, quantization, relevant fine-tuning, optimized batching, to name a few - this flexibility is precisely what your project needs to enhance accuracy and reduce response time. The fact that your project demands diving deep into an existing codebase resonates with my approach to problem-solving. Over the years, I've perfected the art of carefully analyzing systems, understanding their strengths and weaknesses, and then executing targeted optimizations iteratively. By employing similar strategies for your large-language model (LLM), I assure you marked improvements in both precision and inference latency. Choosing me would be more than just getting an AI engineer - it would ensure obtaining a full-stack development team that prides itself on high-quality end-to-end project delivery. I have carried out 1000+ projects across 42+ countries, which makes me comfortable working with diverse requirements & audiences. Let's initiate this collaboration to test the capabilities of human intelligence for AI together – if we succeed, there will be abundant scope for ongoing iteration and exciting feature expansions in the future.

@omarfranciscoa

Hi — I can jump into your existing LLM codebase and training/serving pipeline and deliver measurable gains in accuracy (fewer hallucinations, higher BLEU/F1) and latency without destabilizing production. From your brief, the key challenge is finding the real bottlenecks: where errors originate (data, objective, decoding, retrieval/context, eval gaps) and where time is spent (tokenization, batching, model compute, I/O, serving stack). My approach would be to run a short diagnostic first: reproduce your eval harness, profile inference end-to-end, and segment failures on your labeled set. Then I’d implement the highest-ROI fixes: targeted domain fine-tuning (LoRA/QLoRA or full FT), better decoding/guardrails to reduce hallucinations, and speed improvements via quantization, optimized batching, KV-cache tuning, and (if appropriate) a faster serving stack (vLLM/TGI) with stable rollout. I have hands-on experience tuning and serving LLMs in production, improving factuality/consistency, and reducing latency with practical MLOps-friendly changes. You can expect clear before/after metrics, a concise report of what changed and why, and a pilot plan that de-risks production deployment. If you share current model size, hardware, and baseline BLEU/F1 + p95 latency, I can propose a focused first sprint immediately.

@jaswantp0

Hello, Senior AI/ML engineer with deep expertise in LLM optimization. Delivered 15+ production LLMs with 25%+ accuracy gains (BLEU/F1) and 40% latency reductions through targeted fine-tuning and inference optimizations. My Approach for Your Model: Accuracy: Diagnose hallucinations via evaluation harness → PEFT/LoRA on domain corpus → DPO/ORPO alignment → RAG/distillation if needed. Speed: Quantization (4/8-bit), model pruning, KV cache optimization, batching, vLLM/TensorRT serving stack. Week 1 Plan: Audit current pipeline/checkpoints (overfitting? data quality? eval gaps?) Baseline metrics on your test set Quick-win optimizations (quantization, inference tweaks) Propose 2-3 fine-tuning strategies with expected gains Deliverables: Optimized checkpoints, full pipeline (training/inference), performance report (before/after metrics), reproduction scripts. ₹20,000 for pilot. Strong LLM track record, available immediately. Share repo access—let's beat your targets!