Top Fine-Tuning Engineers

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.

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.

Senior AI/ML Engineer | Generative AI | LLMs | MLOps | Cloud AI I am an AI/ML Engineer with 5+ years of experience designing, building, and deploying scalable machine learning and Generative AI solutions that solve complex business problems. My expertise spans the entire ML lifecycle—from data engineering and model development to MLOps, deployment, monitoring, and continuous optimization. I specialize in developing production-grade AI systems using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), NLP, Computer Vision, and predictive analytics. I have hands-on experience building enterprise AI solutions with technologies such as LangChain, GPT, BERT, LLaMA, Azure ML, AWS SageMaker, Docker, Kubernetes, and MLflow. Throughout my career, I have: * Built and deployed scalable ML and Generative AI applications on Azure and AWS cloud platforms * Developed RAG-based knowledge management systems with semantic search capabilities * Fine-tuned transformer models for NLP tasks including classification and summarization * Designed end-to-end MLOps pipelines for automated training, deployment, monitoring, and retraining * Implemented real-time model monitoring and drift detection frameworks to ensure model reliability * Built computer vision solutions using YOLO and deep learning architectures for real-time inference * Developed predictive analytics and customer segmentation models using advanced machine learning techniques Core Expertise: Generative AI & LLMs (GPT, BERT, LLaMA) Retrieval-Augmented Generation (RAG) & LangChain Machine Learning & Deep Learning NLP & Semantic Search Computer Vision & Real-Time Inference MLOps, CI/CD, Docker, Kubernetes, MLflow Azure ML, AWS SageMaker, Cloud-Native AI Solutions Python, SQL, FastAPI, MongoDB, PostgreSQL I am passionate about leveraging AI to create intelligent, scalable, and impactful solutions that drive business value and innovation. I enjoy collaborating with cross-functional teams, mentoring peers, and staying at the forefront of advancements in AI, machine learning, and Generative AI technologies. I'm always open to connecting with AI professionals, technology leaders, and organizations building the next generation of intelligent systems.

AI/ML Engineer with 5+ years of experience building production machine learning systems across financial services and healthcare. Currently at Capital One, where I fine-tune large language models and build RAG architectures that power customer service automation and fraud detection. Previously at Apollo Hospitals, where I developed computer vision models for medical imaging diagnostics that processed thousands of patient scans daily. My work spans LLM fine-tuning using LoRA and QLoRA, building retrieval-augmented generation pipelines, optimizing model inference for low-latency serving, and deploying end-to-end MLOps workflows on AWS. I've taken models from experimentation to production at scale, working closely with cross-functional teams including software engineers, radiologists, and business stakeholders. I care deeply about building AI that's not just accurate but explainable, scalable, and responsible. Whether it's reducing fraud losses in financial services or helping clinicians catch diseases earlier, I'm driven by the impact these systems create for real users. Always open to connecting with folks working on LLMs, medical AI, or MLOps.

I build AI ML systems that run in production at Fortune 500 scaleMy core work: a large-scale product recognition pipeline deployed on Azure that directly powers several automated customer service workflows across many brands.I also architect scalable AI agent systems that automate multi-step reasoning workflows across business functions and building synthetic data pipelines using generative AI models to solve real data scarcity in production settings. I own the full ML lifecycle solo that's architecture, experimentation, deployment, and monitoring. MS in Artificial Intelligence | DePaul University. Based in Chicago. Open to applied ML and AI engineering role

Results-driven AI/ML Engineer with 3+ years of experience building production ML systems, LLM-powered applications, and cloud-native AI platforms across AWS, Azure, and GCP.I specialize in the full ML lifecycle - from feature engineering and deep learning model training (TensorFlow, PyTorch) to LLM fine-tuning (LoRA/PEFT) with Hugging Face, RAG pipeline development with LangChain and vector databases (Pinecone, ChromaDB), and multi-cloud deployment using SageMaker, Vertex AI, and Azure ML.Currently at Lumen Technologies, I architect end-to-end MLOps pipelines, integrate generative AI solutions, and build observability frameworks that have reduced MTTR by 40%+. Pursuing M.S. in Artificial Intelligence & Business Analytics at the University of South Florida. Certified TensorFlow Developer | Google Professional Machine Learning Engineer Open to connecting with AI/ML professionals, recruiters, and collaborators!

I'm a Principal Software Engineer with 7 years of experience building and evolving a production AI/ML product at Interra Systems — a media tech company specializing in automated captioning, subtitling, and broadcast quality tools. My core work spans NLP, ASR, and applied LLMs — fine-tuning Whisper models for dialect detection, building RAG-based chatbots, designing NLP-driven segmentation APIs, and developing agentic AI systems for automated code review. I've also expanded multilingual captioning support to 20 languages and improved transcription sync accuracy from 94% to 97%. I find the most interesting problems at the intersection of backend engineering and AI — where systems need to be not just accurate, but reliable and scalable in production. Open to roles in: NLP Engineering · Applied AI/ML · Backend Engineering with AI focus

AI/ML Project Assistant at IISc | B.Tech in Data Science | Computer Vision & LLMs I'm a Data Science graduate from Presidency University, currently contributing to Project Chandana at the Indian Institute of Science (IISc), Bangalore working on healthcare AI research focused on Sickle Cell Anemia detection and analysis. My work spans fine-tuning Large Language Models (QLoRA, PEFT), building AI-powered pipelines, and engineering zero-error medical dataset validation systems with 98% experiment reproducibility. I bridge the gap between complex data and actionable insights and I'm driven by the potential of AI to solve real systemic problems. Previously built production-grade backend systems RESTful APIs, Kafka-based distributed architecture, PostgreSQL optimization and CI/CD pipelines. Core Skills: Python · PyTorch · LLM Fine-Tuning · Computer Vision · Java Spring Boot · Kafka · Docker · Data Engineering Bangalore | Open to full-time AI/ML or Full Stack roles | Remote & Relocation welcome