Top Fine-Tuning Engineers

I’m an AI/ML Engineer with over 5 years of experience building and deploying large-scale AI systems across e-commerce, media, and accessibility domains. My work focuses on optimizing LLM fine-tuning, RAG pipelines, and real-time personalization using cloud-native and GPU-accelerated architectures. At Scale AI and Meta, I designed Llama-based inference pipelines, integrated FAISS vector search, and developed data streaming workflows with Kafka and PySpark—achieving measurable improvements in inference speed, accuracy, and customer engagement. I’m passionate about scalable model deployment, GPU optimization, and applied research in GenAI. I love transforming complex machine learning models into production-grade solutions that directly impact user experience and business growth. Core Skills: LLM Fine-Tuning (Llama 2, GPT-4), RAG, PyTorch, DeepSpeed, Triton, FAISS, AWS SageMaker, Kubernetes, Docker, PySpark, MLflow, MLOps

AI/ML Engineer & Full-Stack Developer | 6+ years building intelligent systems I build AI that actually ships — from autonomous LangChain agents and fine-tuned LLMs to multi-modal vision pipelines and quantitative trading engines. My work has saved 3 enterprise clients 1,200+ operational hours per year and cut infrastructure costs by 30%. What I specialise in: - Agentic AI: LangChain, RAG pipelines, tool-use agents, vector databases (Chroma, Pinecone) - LLM Fine-Tuning: LoRA/QLoRA/PEFT on LLaMA 2, Mistral; 18–22% accuracy gains - Multi-modal AI: Vision + LLM pipelines for document understanding and visual QA - Quant Trading: AI-driven algo trading systems, hedge fund strategy modules, Sharpe > 1.4 - MLOps: MLflow, Docker, Kubernetes, AWS/GCP deployment - Full-Stack: Python, Django, DRF, FastAPI, Flutter Previously CTO at Laughing Cabs — built the entire tech stack from zero to production, led a 4-person engineering team. Open to senior AI/ML engineering, quantitative systems, and tech leadership roles.

I’m an AI/ML Engineer with a strong focus on Generative AI, LLM fine-tuning, and MLOps. Over the past few years, I’ve worked with companies like AT&T, Tech Mahindra, and Accenture, building and deploying AI solutions that range from chatbots and copilots to large-scale enterprise automation. I enjoy working end-to-end — from data pipelines and model training to cloud deployment on AWS and Azure. My experience spans Python, TensorFlow, PyTorch, Hugging Face, and LangChain, along with cloud tools like SageMaker, Databricks, and Cognitive Services. I also bring a full-stack development background, which helps me integrate AI models seamlessly into real-world applications. What excites me most is the impact AI can create — whether it’s improving decision-making, automating workflows, or enhancing customer experiences. I’m always curious, always learning, and motivated to turn the latest in Generative AI into practical solutions that make a difference. If you'd like to connect or discuss opportunities, feel free to reach me at: Email: [email protected]

I taught myself Python from scratch in early 2026. A few months later, I was fine-tuning LLMs and shipping ML systems professionally. I'm an ML/AI Engineer at TRON Technologies, where I work on commercial AI products: fine-tuning LLMs (Qwen family, LoRA/QLoRA), building RAG pipelines, generating and cleaning training datasets, and running training infrastructure on AWS. That includes keeping multi-hour runs stable when environments and dependencies fight back. I like owning a model end to end, from data prep through evaluation. I also build systems-heavy Python applications the disciplined way: config-driven design, logic separated from rendering, additive schemas, and a test suite past 1,200 cases. My math side is real too, as I'm finishing a B.S. in Computational Mathematics with a 4.0. I'm looking for remote AI/ML engineering roles where I can keep building real systems. If you're hiring, let's talk. Skills: Python, PyTorch, LLM fine-tuning, LoRA/QLoRA, RAG, Hugging Face Transformers, AWS (EC2, S3), data pipelines, model evaluation

Application Architect with deep expertise in Artificial Intelligence, specializing in LLM fine-tuning, embedding model optimization, Agentic AI, edge agent and personal agent design and development, and Applied Cryptology. Proven ability to design, secure, and scale intelligent systems across cloud and on-premise environments. Strong background in Site Reliability Engineering (SRE) and DevOps, with hands-on experience in AWS Cloud, Kubernetes, PCF, and end-to-end automation. Expert in on-premise and edge LLM setup, enabling low-latency, privacy-preserving AI solutions for enterprise and regulated environments. Highly skilled in HashiCorp Vault and enterprise secret management, GitLab CI/CD, Python programming, and Linux, with a strong focus on security-first and reliability-driven architectures. Extensive experience implementing Apollo GraphQL Federation to build scalable, distributed APIs. Proven track record in enterprise application integration and modernization, including Oracle HCM Cloud, PeopleSoft implementations and upgrades, Taleo, and complex database systems. Passionate about architecting cloud-native and on-premise edge AI solutions, with deep expertise in LLM fine-tuning, edge LLM deployments, and intelligent agent systems, delivering reliability, security, and measurable business impact. - Indian Institute of Technology Madras (IIT M) and KTH Royal Institute of Technology, Sweden certified Machine Learning professional.

AI/ML Engineer at zeb working in Artificial Intelligence with a specialization in Model Building and Fine-Tuning, focused on developing high-performance models and improving them for real-world applications.Focused on advancing intelligent systems through efficient model development, optimization, and experimentation.

Hi, I am Sai Syam, currently pursuing MTech in Computer Science from Sri Sathya Sai Institute of Higher Learning(SSSIHL).