Hire the Best Machine Learning Engineers in Bengaluru, IN

I am an engineer having a bachelor's degree in computer science engineering, experienced in Machine learning and deep learning for the last 3 years and have been working on various projects on the same technology constantly learning and applying my knowledge in this fast-growing and fast-developing field of Artificial Intelligence.

Most people building LLM products have never trained one. That's why the fixes stop at the prompt, when retrieval quietly degrades, cost per conversation triples, or quality regresses, and nobody notices. The problem is underneath the API, and that's where I work. Five years training language models, and five years shipping systems built on them. That combination is why teams call me when what they already built stops holding up. WHAT I DO 1) Production LLM systems - RAG, agents, serving Retrieval that actually retrieves: 92% retrieval accuracy on a LlamaIndex + Weaviate pipeline with a 20% cut in query time. Chunking, embedding choice, hybrid and reranked retrieval - and an eval set that proves the change helped instead of a vibe check. Agent systems with a cost and latency budget: multi-agent pipelines in LangChain / LlamaIndex / CrewAI, tool calling, long-running state. One automated product-information system cut manual review effort ~90%. Self-hosted and open-weight serving: GPU sizing, quantisation strategy, throughput and concurrency planning, and quality validation against a frontier baseline before you cut over. At Dell I shipped 4-bit GPTQ quantisation and SparseGPT pruning (~40% sparsity) for hardware-constrained inference. Latency and cost: replacing an LLM call with a fine-tuned 300M classifier took one production path from ~1s to ~100ms. Model routing, caching, honest per-request cost accounting. Evaluation and regression gates: offline eval sets, LLM-judge calibration, CI gates so a prompt or model change can't silently regress. Most teams I meet have no way to answer "is it better than last week." 2) Fine-tuning, post-training and alignment Pre-trained a 355M-parameter GPT-2-medium architecture from scratch on 28B tokens (Cosmopedia-v2), distributed across 4x NVIDIA H100's with DeepSpeed - mixed precision, gradient accumulation, LR scheduling. Beat the original GPT-2-medium checkpoint on perplexity. Improved Phi-4-14B-Instruct by 2% across every Hugging Face leaderboard benchmark via Model Stock merging, validated cheaply first on a LoRA-tuned Qwen2.5-1.5B proxy over 1.2M curated STEM samples. LoRA-tuned Qwen2.5-14B-Instruct on 12K reasoning samples, using synthetic data from a multi-agent generation pipeline - measured gains on GSM8K, GPQA, and MMLU. LoRA + DPO on Llama-2 for customer-care summarisation (23K SFT samples, 5K preference pairs): 17% better across evaluation metrics. Designed and ablated a novel Drift-Diffusion attention mechanism on BERT-base, with full Weights & Biases tracking across baseline, unscaled and gated variants. The honest version: most projects that arrive asking for a fine-tune don't need one. The base model was already good enough, the eval set couldn't detect improvement, or the problem was retrieval. I'll tell you which before you spend GPU budget - that answer is worth more than the training run. STACK Python, PyTorch, Hugging Face, DeepSpeed, Weights & Biases, FastAPI, LangChain, LlamaIndex, CrewAI, Weaviate, Elasticsearch, MongoDB, Docker, Kubernetes, AWS (EC2, Inferentia-2), GCP. OpenAI, Anthropic, Gemma / Llama / Mistral / Qwen / Phi. ElevenLabs and LiveKit for voice. BACKGROUND ML Engineer at Dell Technologies and BYJU'S AI Labs, where a multi-objective Transformer recommender I built served 1M+ students across 1B+ data points at 85% F1. Contributor to Hugging Face Transformers documentation and to DocsGPT. Top-Rated on Upwork with a 100% Job Success Score. WHO I'M NOT FOR If the job is wiring Zapier or n8n between two SaaS tools, hire a generalist - genuinely, you'll get a better deal and a faster one. I'm worth the rate when the system has to be correct, cheap and measurable under real traffic. HOW TO START Start with the fixed-fee diagnostic rather than an open-ended hourly build. I read your pipeline, your traces and your evaluation setup, then send a written diagnosis: where quality is leaking, what each request actually costs, what to fix first, and what fixing it takes. It stands on its own as a deliverable, and it becomes the scope if you want me to do the build. Send me what's breaking and one example of the wrong output. That's enough to start.

I am an Machine Learning Engineer/ AI Application Developer with experience in backend development, computer vision, and machine learning. 1) Expertise in building scalable services and data pipelines with AWS, Flask, Docker, Kubernetes, and CI/CD 2) Building machine learning models for computer vision, NLP, Multimodal tasks. Expertise with LLMs and Vision models.

Hi, I’m Abhay 👋 Give me a problem to solve, and I’ll design and build an intelligent system that works for you 24/7. That’s my promise. If you send me an invite or message, I’ll reply with a personalized breakdown of how I’d approach your project including scope, architecture, and execution plan. What I Do: I help startups, founders, and growing teams turn ideas into real, production-ready AI and web products. Whether you need: • An AI model integrated into your product • A machine learning prototype • A data automation system • Or a full-stack SaaS built from scratch I take ownership from idea → scope → prototype → deployment. What Makes Me Different: Many developers jump straight into coding. I start with: • Clear requirement gathering • Structured problem breakdown • Thoughtful system design • Lean prototyping before scaling • Realistic roadmap & milestone planning How I Help You If you’re: • Sitting on data but not extracting value • Building an AI feature but unsure how to architect it • Managing manual workflows that should be automated • Planning a product and need technical clarity I’ll help you define the right solution before building it, then implement it cleanly. Core Expertise: • AI & LLM Systems: LLM applications & agents, Prompt engineering & RAG pipelines, AI workflow automation, Custom ML models (time series, forecasting), Model evaluation & optimization • Backend & APIs: Python, FastAPI, Flask, REST APIs, SQL & database design, Automation workflows • Frontend & Full-Stack: Next.js, React, Node.js, SaaS development, Cloud deployment What You Can Expect : Clear communication | Structured project planning | Clean, maintainable code | Fast prototyping | Strong architectural thinking | On-time deliver

🚀 Machine Learning Engineer | Generative AI & Agentic Systems | LLM Specialist I am a Machine Learning Engineer with 3+ years of experience in building and deploying Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Agentic AI applications, and Data Science solutions. Skilled in Python and Deep Learning, I specialize in transforming cutting-edge research into production-ready AI systems that deliver measurable business impact. 💡 Core Expertise LLMs & Generative AI: Fine-tuning, optimization, and custom pipeline design with Hugging Face, LangChain, LlamaIndex, CrewAI RAG Applications: Architecting retrieval-augmented generation systems for high-accuracy, knowledge-intensive tasks Agentic AI Systems: Designing and deploying multi-agent workflows for autonomous reasoning and decision-making Data Science & Analytics: Data wrangling, feature engineering, predictive modeling, and visualization for insight-driven solutions Backend & APIs: Developing production-ready AI services with FastAPI, Flask, Streamlit MLOps & Cloud: CI/CD pipelines, containerization with Docker, and scalable deployment on AWS & GCP 🛠️ Technical Toolkit Languages: Python (primary), SQL Frameworks: PyTorch, scikit-learn, pandas, NumPy LLM Ecosystem: Hugging Face, LangChain, LlamaIndex, CrewAI APIs & Deployment: FastAPI, Flask, Streamlit, Docker Databases: Pinecone, Chroma (Vector DBs), PostgreSQL Cloud & MLOps: AWS, GCP, CI/CD pipelines Visualization & Prototyping: Gradio, Streamlit, matplotlib 🏆 Notable Contributions Designed and deployed RAG-powered assistants that improved factual accuracy and reliability of LLM responses Built agentic AI systems using CrewAI to automate reasoning and multi-agent collaboration Delivered end-to-end ML pipelines, from LLM fine-tuning to scalable API deployment Applied data science workflows (EDA, predictive modeling, visualization) to extract insights and drive decision-making Improved performance of large-scale AI models in real-world applications through optimization and benchmarking 🌟 Why Collaborate With Me? With 3+ years of experience at the intersection of Generative AI, Data Science, and Applied ML Engineering, I bring both research-driven innovation and production engineering expertise. From Python-based data pipelines to cloud-deployed agentic AI applications, I am passionate about building scalable, reliable, and impactful AI solutions.

Hello! I'm a Generative AI and Machine Learning Practitioner With over 6 years of experience in Machine Learning and Data Science, I specialize in building state-of-the-art Gen AI and Computer Vision applications that drive real business impact. What I do: Build Generative AI applications (RAG systems, LLM fine-tuning, document Q&A bots) using LangChain, Hugging Face, OpenAI, and vector databases. Develop Computer Vision models for tasks like object detection, face recognition, and image classification using YOLO, CNNs, and OpenCV. Use advanced data analytics and statistical modeling (Bayesian, predictive analytics) to help businesses make smart, data-driven decisions. Design and deploy robust, production-grade ML and Gen AI applications on Azure using services like Azure Web Apps, Azure Kubernetes Service (AKS), Azure ML, and Azure AI Foundry. My motto is to help you turn your data into production-ready AI solutions, ensuring both speed and reliably. 💼 Tools I Use: Python, PyTorch, TensorFlow, OpenCV, Azure ML, LangChain, Hugging Face, FastAPI, Power BI, PySpark, SQL, R (BRMS) and more.... Let’s talk if you’re looking to: Build or scale your AI/ML product Experiment with Generative AI for your business use-case Develop a custom computer vision solution Migrate or deploy models on Azure cloud